{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "环状条形图(Circular barplot)是条形图的变体，图如其名，环状条形图在视觉上很吸引人，但也必须小心使用，因为环状条形图使用的是极坐标系而不是笛卡尔坐标系，每一个类别不共享相同的Y轴。环状条形图非常适合于周期性数据，本文主要介绍基于R语言实现环状条形图的绘制。本文主要参考链接:[Circular barplot](https://www.r-graph-gallery.com/circular-barplot.html)\n",
    "\n",
    "\n",
    "R语言的环状条形图主要基于tidyverse包实现，tidyverse是一组R包的集合，这些R包共享共同的原理并旨在无缝地协同工作，具体介绍见：\n",
    "[tidyverse](https://github.com/tidyverse)\n",
    "\n",
    "安装命令如下：\n",
    "> install.packages(\"tidyverse\")\n",
    "\n",
    "本文所有代码见：[R-Study-Notes](https://github.com/luohenyueji/R-Study-Notes/tree/master/Visualization)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 基础环状条形图绘制 Basic circular barplot\n",
    "## 1.1 最基础环状条形图的绘制 Most basic circular barplot\n",
    "环状条形图就是条形图，只不过环状条形图沿圆形而不是直线显示。\n",
    "输入数据集与条形图的输入数据集相同：每个组需要一个数值（一个组=一个条形图）。（请参阅条形图部分的更多说明）。\n",
    "基本上，这个方法和做一个经典的条形图是一样的。最后，我们调用coord_polar()使整个坐标系变为极坐标系，这样会使得图表呈圆形。注意，ylim()参数非常重要。如果它从0开始，这些条将从圆的中心开始。如果您提供负值，将出现一个白色的圆圈空格！此外会用到rep函数，具体介绍见[R中rep函数的使用](https://www.cnblogs.com/business-analysis/p/3414997.html)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>id</th><th scope=col>individual</th><th scope=col>value</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>Mister 1</td><td>44</td></tr>\n",
       "\t<tr><td>2</td><td>Mister 2</td><td>79</td></tr>\n",
       "\t<tr><td>3</td><td>Mister 3</td><td>81</td></tr>\n",
       "\t<tr><td>4</td><td>Mister 4</td><td>62</td></tr>\n",
       "\t<tr><td>5</td><td>Mister 5</td><td>91</td></tr>\n",
       "\t<tr><td>6</td><td>Mister 6</td><td>50</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       " id & individual & value\\\\\n",
       " <int> & <fct> & <int>\\\\\n",
       "\\hline\n",
       "\t 1 & Mister 1 & 44\\\\\n",
       "\t 2 & Mister 2 & 79\\\\\n",
       "\t 3 & Mister 3 & 81\\\\\n",
       "\t 4 & Mister 4 & 62\\\\\n",
       "\t 5 & Mister 5 & 91\\\\\n",
       "\t 6 & Mister 6 & 50\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| id &lt;int&gt; | individual &lt;fct&gt; | value &lt;int&gt; |\n",
       "|---|---|---|\n",
       "| 1 | Mister 1 | 44 |\n",
       "| 2 | Mister 2 | 79 |\n",
       "| 3 | Mister 3 | 81 |\n",
       "| 4 | Mister 4 | 62 |\n",
       "| 5 | Mister 5 | 91 |\n",
       "| 6 | Mister 6 | 50 |\n",
       "\n"
      ],
      "text/plain": [
       "  id individual value\n",
       "1 1  Mister 1   44   \n",
       "2 2  Mister 2   79   \n",
       "3 3  Mister 3   81   \n",
       "4 4  Mister 4   62   \n",
       "5 5  Mister 5   91   \n",
       "6 6  Mister 6   50   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Libraries\n",
    "# 导入包\n",
    "library(tidyverse)\n",
    " \n",
    "# Create dataset\n",
    "# 创建数据\n",
    "data <- data.frame(\n",
    "  id=seq(1,60),\n",
    "  individual=paste( \"Mister \", seq(1,60), sep=\"\"),\n",
    "  value=sample( seq(10,100), 60, replace=T)\n",
    ")\n",
    "head(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aGovTtxtUH6h1xfuGECIlmTYu1OXHGQ/mdaY7hhgjJ2JM3VrbjsIP3PqM5wwwTl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\nJEIS4CARkgAHiZAE/gObjDtxaO+R3AAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make the plot\n",
    "# 画图\n",
    "p <- ggplot(data, aes(x=as.factor(id), y=value)) +       \n",
    "    # This add the bars with a blue color\n",
    "    # 添加蓝色条形，stat表示数据统计方式，也就是说identity提取横坐标x对应的y值\n",
    "    geom_bar(stat=\"identity\", fill=alpha(\"blue\", 0.3)) +\n",
    "    # The negative value controls the size of the inner circle, the positive one is useful to add size over each bar\n",
    "    # 设置y的范围，负值设定内圆的大小，正值设定各个条柱的最高高度\n",
    "    ylim(-100,120)+\n",
    "    # theme_minimal简约主题\n",
    "    theme_minimal() +\n",
    "    # Custom the theme: no axis title and no cartesian grid\n",
    "    # 自定义主题\n",
    "    theme(\n",
    "        # 移除标题坐标文字\n",
    "        axis.text = element_blank(),\n",
    "        axis.title = element_blank(),\n",
    "        # 移除网格\n",
    "        panel.grid = element_blank(),\n",
    "        # This remove unnecessary margin around plot\n",
    "        # 移除不必要空白\n",
    "        plot.margin = unit(rep(-2,4), \"cm\"))+\n",
    "    # This makes the coordinate polar instead of cartesian.\n",
    "    # 使用极坐标系\n",
    "    coord_polar(start = 0)\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 给环状条形图添加标签 Add labels to circular barplot\n",
    "\n",
    "上节说明了如何制作基本的环状条形图。下一步是在每个条上添加标签，以便深入了解图形。这里我建议一种方法，在每个条的顶部添加标签，使用与条中心部分相同的角度。在下面的代码中，有一小段创建了一个带有每个标签特性的数据帧，然后我们可以在geom_text（）中调用它。\n",
    "请注意，为了让标签的更好阅读，这就需要将其中一些标签翻转180度。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**首先添加数据**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "60"
      ],
      "text/latex": [
       "60"
      ],
      "text/markdown": [
       "60"
      ],
      "text/plain": [
       "[1] 60"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>id</th><th scope=col>individual</th><th scope=col>value</th><th scope=col>hjust</th><th scope=col>angle</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>Mister 1</td><td>86</td><td>0</td><td>87</td></tr>\n",
       "\t<tr><td>2</td><td>Mister 2</td><td>81</td><td>0</td><td>81</td></tr>\n",
       "\t<tr><td>3</td><td>Mister 3</td><td>27</td><td>0</td><td>75</td></tr>\n",
       "\t<tr><td>4</td><td>Mister 4</td><td>48</td><td>0</td><td>69</td></tr>\n",
       "\t<tr><td>5</td><td>Mister 5</td><td>57</td><td>0</td><td>63</td></tr>\n",
       "\t<tr><td>6</td><td>Mister 6</td><td>49</td><td>0</td><td>57</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       " id & individual & value & hjust & angle\\\\\n",
       " <int> & <fct> & <int> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1 & Mister 1 & 86 & 0 & 87\\\\\n",
       "\t 2 & Mister 2 & 81 & 0 & 81\\\\\n",
       "\t 3 & Mister 3 & 27 & 0 & 75\\\\\n",
       "\t 4 & Mister 4 & 48 & 0 & 69\\\\\n",
       "\t 5 & Mister 5 & 57 & 0 & 63\\\\\n",
       "\t 6 & Mister 6 & 49 & 0 & 57\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 5\n",
       "\n",
       "| id &lt;int&gt; | individual &lt;fct&gt; | value &lt;int&gt; | hjust &lt;dbl&gt; | angle &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 1 | Mister 1 | 86 | 0 | 87 |\n",
       "| 2 | Mister 2 | 81 | 0 | 81 |\n",
       "| 3 | Mister 3 | 27 | 0 | 75 |\n",
       "| 4 | Mister 4 | 48 | 0 | 69 |\n",
       "| 5 | Mister 5 | 57 | 0 | 63 |\n",
       "| 6 | Mister 6 | 49 | 0 | 57 |\n",
       "\n"
      ],
      "text/plain": [
       "  id individual value hjust angle\n",
       "1 1  Mister 1   86    0     87   \n",
       "2 2  Mister 2   81    0     81   \n",
       "3 3  Mister 3   27    0     75   \n",
       "4 4  Mister 4   48    0     69   \n",
       "5 5  Mister 5   57    0     63   \n",
       "6 6  Mister 6   49    0     57   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Libraries\n",
    "library(tidyverse)\n",
    " \n",
    "# Create dataset\n",
    "# 创建数据\n",
    "data <- data.frame(\n",
    "  id=seq(1,60),\n",
    "  individual=paste( \"Mister \", seq(1,60), sep=\"\"),\n",
    "  value=sample( seq(10,100), 60, replace=T)\n",
    ")\n",
    "# ----- This section prepare a dataframe for labels ---- #\n",
    "# 准备数据标签\n",
    "# Get the name and the y position of each label\n",
    "label_data <- data\n",
    "# calculate the ANGLE of the labels\n",
    "# 计算标签角度\n",
    "number_of_bar <- nrow(label_data)\n",
    "number_of_bar\n",
    "\n",
    "# I substract 0.5 because the letter must have the angle of the center of the bars. Not extreme right(1) or extreme left (0)\n",
    "# 减去0.5是为了让标签位于条柱中心\n",
    "# angle是标签角度\n",
    "angle <-  90 - 360 * (label_data$id-0.5) /number_of_bar \n",
    "\n",
    "# calculate the alignment of labels: right or left\n",
    "# If I am on the left part of the plot, my labels have currently an angle < -90\n",
    "# 判断标签左对齐还是右对齐，也就是标签是朝向左边还是右边\n",
    "label_data$hjust<-ifelse( angle < -90, 1, 0)\n",
    " \n",
    "# flip angle BY to make them readable\n",
    "# 翻转标签\n",
    "label_data$angle<-ifelse(angle < -90, angle+180, angle)\n",
    "# ----- ------------------------------------------- ---- #\n",
    "head(label_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**开始绘图**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wGmZ93WeVByWZVNc+P2X/aDT0lZQjKj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\nyWuL2c3eCRVcRk8kh3rHcTiBkAx13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cFFFbR5cUUoi5dIrJ1xEW8UpS0VZuKSSQ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\nRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Start the plot\n",
    "# 开始绘图\n",
    "p <- ggplot(data, aes(x=as.factor(id), y=value)) +   \n",
    "    # This add the bars with a bskyblue color\n",
    "    # 添加蓝色条形，stat表示数据统计方式，也就是说identity提取横坐标x对应的y值\n",
    "    geom_bar(stat=\"identity\", fill=alpha(\"skyblue\", 0.7)) +\n",
    "\n",
    "    # The negative value controls the size of the inner circle, the positive one is useful to add size over each bar\n",
    "    # 设置y的范围，负值设定内圆的大小，正值设定各个条柱的最高高度\n",
    "    ylim(-100,120)+\n",
    "\n",
    "    # theme_minimal简约主题\n",
    "    theme_minimal() +\n",
    "    # Custom the theme: no axis title and no cartesian grid\n",
    "    # 自定义主题\n",
    "    theme(\n",
    "        # 移除标题坐标文字\n",
    "        axis.text = element_blank(),\n",
    "        axis.title = element_blank(),\n",
    "        # 移除网格\n",
    "        panel.grid = element_blank(),\n",
    "        # This remove unnecessary margin around plot\n",
    "        # 移除不必要空白\n",
    "        plot.margin = unit(rep(-2,4), \"cm\"))+\n",
    "\n",
    "    # This makes the coordinate polar instead of\n",
    "    # 设置极坐标系\n",
    "    coord_polar(start = 0) +\n",
    "\n",
    "    # Add the labels, using the label_data dataframe that we have created before\n",
    "    # 添加标签\n",
    "    geom_text(data=label_data, aes(x=id, y=value+10, label=individual, hjust=hjust), color=\"black\", fontface=\"bold\",alpha=0.6, size=2.5, angle= label_data$angle, inherit.aes = FALSE ) \n",
    "\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 分组环状条形图 Circular barplot with groups\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 在圆中添加间隙 Add a gap in the circle\n",
    "本节主要介绍在圆中添加间隙，其实大部分操作和上一节一样，只是在初始数据帧的末尾添加了几行空行就能添加间隙"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**添加数据**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>individual</th><th scope=col>value</th><th scope=col>id</th><th scope=col>hjust</th><th scope=col>angle</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Mister 1</td><td>18</td><td>1</td><td>0</td><td>87.42857</td></tr>\n",
       "\t<tr><td>Mister 2</td><td>55</td><td>2</td><td>0</td><td>82.28571</td></tr>\n",
       "\t<tr><td>Mister 3</td><td>69</td><td>3</td><td>0</td><td>77.14286</td></tr>\n",
       "\t<tr><td>Mister 4</td><td>36</td><td>4</td><td>0</td><td>72.00000</td></tr>\n",
       "\t<tr><td>Mister 5</td><td>46</td><td>5</td><td>0</td><td>66.85714</td></tr>\n",
       "\t<tr><td>Mister 6</td><td>82</td><td>6</td><td>0</td><td>61.71429</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       " individual & value & id & hjust & angle\\\\\n",
       " <fct> & <int> & <int> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Mister 1 & 18 & 1 & 0 & 87.42857\\\\\n",
       "\t Mister 2 & 55 & 2 & 0 & 82.28571\\\\\n",
       "\t Mister 3 & 69 & 3 & 0 & 77.14286\\\\\n",
       "\t Mister 4 & 36 & 4 & 0 & 72.00000\\\\\n",
       "\t Mister 5 & 46 & 5 & 0 & 66.85714\\\\\n",
       "\t Mister 6 & 82 & 6 & 0 & 61.71429\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 5\n",
       "\n",
       "| individual &lt;fct&gt; | value &lt;int&gt; | id &lt;int&gt; | hjust &lt;dbl&gt; | angle &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| Mister 1 | 18 | 1 | 0 | 87.42857 |\n",
       "| Mister 2 | 55 | 2 | 0 | 82.28571 |\n",
       "| Mister 3 | 69 | 3 | 0 | 77.14286 |\n",
       "| Mister 4 | 36 | 4 | 0 | 72.00000 |\n",
       "| Mister 5 | 46 | 5 | 0 | 66.85714 |\n",
       "| Mister 6 | 82 | 6 | 0 | 61.71429 |\n",
       "\n"
      ],
      "text/plain": [
       "  individual value id hjust angle   \n",
       "1 Mister 1   18    1  0     87.42857\n",
       "2 Mister 2   55    2  0     82.28571\n",
       "3 Mister 3   69    3  0     77.14286\n",
       "4 Mister 4   36    4  0     72.00000\n",
       "5 Mister 5   46    5  0     66.85714\n",
       "6 Mister 6   82    6  0     61.71429"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# library\n",
    "library(tidyverse)\n",
    " \n",
    "# Create dataset\n",
    "# 添加数据\n",
    "data <- data.frame(\n",
    "  individual=paste( \"Mister \", seq(1,60), sep=\"\"),\n",
    "  value=sample( seq(10,100), 60, replace=T)\n",
    ")\n",
    " \n",
    "# Set a number of 'empty bar'\n",
    "# 设置空白柱的个数\n",
    "empty_bar <- 10\n",
    " \n",
    "# 在原始数据中添加空白数据\n",
    "# Add lines to the initial dataset\n",
    "to_add <- matrix(NA, empty_bar, ncol(data))\n",
    "colnames(to_add) <- colnames(data)\n",
    "data <- rbind(data, to_add)\n",
    "data$id <- seq(1, nrow(data))\n",
    "\n",
    "# Get the name and the y position of each label\n",
    "# 和上一步一样，获得标签角度信息\n",
    "label_data <- data\n",
    "number_of_bar <- nrow(label_data)\n",
    "angle <- 90 - 360 * (label_data$id-0.5) /number_of_bar   \n",
    "label_data$hjust <- ifelse( angle < -90, 1, 0)\n",
    "label_data$angle <- ifelse(angle < -90, angle+180, angle)\n",
    "head(label_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**绘图**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 10 rows containing missing values (position_stack).\"\n",
      "Warning message:\n",
      "\"Removed 10 rows containing missing values (geom_text).\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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HBEYm5IGkV0RPqESG/LmJV5dCYmNSId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NIhyRJ0Re0zwj8+lqcIDEOBI7inXm5VbDkdCe0OFV31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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make the plot\n",
    "# 绘图\n",
    "p <- ggplot(data, aes(x=as.factor(id), y=value)) +       # Note that id is a factor. If x is numeric, there is some space between the first bar\n",
    "  geom_bar(stat=\"identity\", fill=alpha(\"green\", 0.3)) +\n",
    "  ylim(-100,120) +\n",
    "  theme_minimal() +\n",
    "  theme(\n",
    "    axis.text = element_blank(),\n",
    "    axis.title = element_blank(),\n",
    "    panel.grid = element_blank(),\n",
    "    plot.margin = unit(rep(-1,4), \"cm\") \n",
    "  ) +\n",
    "  coord_polar(start = 0) + \n",
    "  geom_text(data=label_data, aes(x=id, y=value+10, label=individual, hjust=hjust), color=\"black\", fontface=\"bold\",alpha=0.6, size=2.5, angle= label_data$angle, inherit.aes = FALSE ) \n",
    " \n",
    "p;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 组间距设置 Space between groups\n",
    "组间距就是在各个组之间添加若干个空白柱，本节代码会用到R语言管道，具体介绍[R语言中的管道%>%](https://www.jianshu.com/p/c65dbce983dd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**首先创建一个空白数组**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>individual</th><th scope=col>group</th><th scope=col>value</th></tr>\n",
       "\t<tr><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;lgl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>NA</td><td>A</td><td>NA</td></tr>\n",
       "\t<tr><td>NA</td><td>A</td><td>NA</td></tr>\n",
       "\t<tr><td>NA</td><td>A</td><td>NA</td></tr>\n",
       "\t<tr><td>NA</td><td>A</td><td>NA</td></tr>\n",
       "\t<tr><td>NA</td><td>B</td><td>NA</td></tr>\n",
       "\t<tr><td>NA</td><td>B</td><td>NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       " individual & group & value\\\\\n",
       " <lgl> & <chr> & <lgl>\\\\\n",
       "\\hline\n",
       "\t NA & A & NA\\\\\n",
       "\t NA & A & NA\\\\\n",
       "\t NA & A & NA\\\\\n",
       "\t NA & A & NA\\\\\n",
       "\t NA & B & NA\\\\\n",
       "\t NA & B & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| individual &lt;lgl&gt; | group &lt;chr&gt; | value &lt;lgl&gt; |\n",
       "|---|---|---|\n",
       "| NA | A | NA |\n",
       "| NA | A | NA |\n",
       "| NA | A | NA |\n",
       "| NA | A | NA |\n",
       "| NA | B | NA |\n",
       "| NA | B | NA |\n",
       "\n"
      ],
      "text/plain": [
       "  individual group value\n",
       "1 NA         A     NA   \n",
       "2 NA         A     NA   \n",
       "3 NA         A     NA   \n",
       "4 NA         A     NA   \n",
       "5 NA         B     NA   \n",
       "6 NA         B     NA   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# library\n",
    "library(tidyverse)\n",
    " \n",
    "# Create dataset\n",
    "# 创建数据集\n",
    "data <- data.frame(\n",
    "  individual=paste( \"Mister \", seq(1,60), sep=\"\"),\n",
    "  group=c( rep('A', 10), rep('B', 30), rep('C', 14), rep('D', 6)) ,\n",
    "  value=sample( seq(10,100), 60, replace=T)\n",
    ")\n",
    "\n",
    "# Set a number of 'empty bar' to add at the end of each group\n",
    "# 在原始数据中添加空白数据\n",
    "# empty_bar 表示组之间的空白距离\n",
    "empty_bar <- 4\n",
    "# 每一组之间4个空白\n",
    "to_add <- data.frame( matrix(NA, empty_bar*nlevels(data$group), ncol(data)) )\n",
    "colnames(to_add) <- colnames(data)\n",
    "# 为每个空白值提供组信息，rep函数的意思就是复制值，levels(data$group)为复制的对象，each为复制的次数\n",
    "to_add$group <- rep(levels(data$group), each=empty_bar)\n",
    "head(to_add)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**然后将空白数组与原始数据绑定**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>individual</th><th scope=col>group</th><th scope=col>value</th><th scope=col>id</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Mister 1</td><td>A</td><td>79</td><td>1</td></tr>\n",
       "\t<tr><td>Mister 2</td><td>A</td><td>20</td><td>2</td></tr>\n",
       "\t<tr><td>Mister 3</td><td>A</td><td>67</td><td>3</td></tr>\n",
       "\t<tr><td>Mister 4</td><td>A</td><td>47</td><td>4</td></tr>\n",
       "\t<tr><td>Mister 5</td><td>A</td><td>78</td><td>5</td></tr>\n",
       "\t<tr><td>Mister 6</td><td>A</td><td>50</td><td>6</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       " individual & group & value & id\\\\\n",
       " <fct> & <fct> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t Mister 1 & A & 79 & 1\\\\\n",
       "\t Mister 2 & A & 20 & 2\\\\\n",
       "\t Mister 3 & A & 67 & 3\\\\\n",
       "\t Mister 4 & A & 47 & 4\\\\\n",
       "\t Mister 5 & A & 78 & 5\\\\\n",
       "\t Mister 6 & A & 50 & 6\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| individual &lt;fct&gt; | group &lt;fct&gt; | value &lt;int&gt; | id &lt;int&gt; |\n",
       "|---|---|---|---|\n",
       "| Mister 1 | A | 79 | 1 |\n",
       "| Mister 2 | A | 20 | 2 |\n",
       "| Mister 3 | A | 67 | 3 |\n",
       "| Mister 4 | A | 47 | 4 |\n",
       "| Mister 5 | A | 78 | 5 |\n",
       "| Mister 6 | A | 50 | 6 |\n",
       "\n"
      ],
      "text/plain": [
       "  individual group value id\n",
       "1 Mister 1   A     79    1 \n",
       "2 Mister 2   A     20    2 \n",
       "3 Mister 3   A     67    3 \n",
       "4 Mister 4   A     47    4 \n",
       "5 Mister 5   A     78    5 \n",
       "6 Mister 6   A     50    6 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnames(to_add) <- colnames(data)\n",
    "to_add$group <- rep(levels(data$group), each=empty_bar)\n",
    "data <- rbind(data, to_add)\n",
    "# 管道操作类似 data<-arrange(data,data$group)\n",
    "data <- data %>% arrange(group)\n",
    "# 设置id\n",
    "data$id <- seq(1, nrow(data))\n",
    "head(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**绘图**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 16 rows containing missing values (position_stack).\"\n",
      "Warning message:\n",
      "\"Removed 16 rows containing missing values (geom_text).\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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MXJ9b8RpHSSQq7leazv9PcPZtovqYUgJXE027HTDHcgfTNJXwlSKklhIh7Hzn6y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\nSPSExoqVv7bjhjHT8pQaq55KxhV40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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the name and the y position of each label\n",
    "# 设定角度值\n",
    "label_data <- data\n",
    "number_of_bar <- nrow(label_data)\n",
    "angle <- 90 - 360 * (label_data$id-0.5) /number_of_bar    \n",
    "label_data$hjust <- ifelse( angle < -90, 1, 0)\n",
    "label_data$angle <- ifelse(angle < -90, angle+180, angle)\n",
    " \n",
    "# Make the plot\n",
    "# fill 按组填充颜色\n",
    "p <- ggplot(data, aes(x=as.factor(id), y=value, fill=group)) +   \n",
    "  geom_bar(stat=\"identity\", alpha=0.5) +\n",
    "  ylim(-100,120) +\n",
    "  theme_minimal() +\n",
    "  theme(\n",
    "    legend.position = \"none\",\n",
    "    axis.text = element_blank(),\n",
    "    axis.title = element_blank(),\n",
    "    panel.grid = element_blank(),\n",
    "    plot.margin = unit(rep(-1,4), \"cm\") \n",
    "  ) +\n",
    "  coord_polar() + \n",
    "  geom_text(data=label_data, aes(x=id, y=value+10, label=individual, hjust=hjust), color=\"black\", fontface=\"bold\",alpha=0.6, size=2.5, angle= label_data$angle, inherit.aes = FALSE ) \n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 对柱状进行排序 Order bars\n",
    "在这里，观察结果是按每个组内的条形高度排序的。如果您的目标是了解组内和组间的最高/最低观察值是什么，那么这将非常有用。在上一节中修改一行代码即可：\n",
    "```\n",
    "# data = data %>% arrange(group)\n",
    "# 修改为\n",
    "data = data %>% arrange(group, value)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>individual</th><th scope=col>group</th><th scope=col>value</th></tr>\n",
       "\t<tr><th scope=col>&lt;lgl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;lgl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>NA</td><td>A</td><td>NA</td></tr>\n",
       "\t<tr><td>NA</td><td>A</td><td>NA</td></tr>\n",
       "\t<tr><td>NA</td><td>A</td><td>NA</td></tr>\n",
       "\t<tr><td>NA</td><td>A</td><td>NA</td></tr>\n",
       "\t<tr><td>NA</td><td>B</td><td>NA</td></tr>\n",
       "\t<tr><td>NA</td><td>B</td><td>NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       " individual & group & value\\\\\n",
       " <lgl> & <chr> & <lgl>\\\\\n",
       "\\hline\n",
       "\t NA & A & NA\\\\\n",
       "\t NA & A & NA\\\\\n",
       "\t NA & A & NA\\\\\n",
       "\t NA & A & NA\\\\\n",
       "\t NA & B & NA\\\\\n",
       "\t NA & B & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| individual &lt;lgl&gt; | group &lt;chr&gt; | value &lt;lgl&gt; |\n",
       "|---|---|---|\n",
       "| NA | A | NA |\n",
       "| NA | A | NA |\n",
       "| NA | A | NA |\n",
       "| NA | A | NA |\n",
       "| NA | B | NA |\n",
       "| NA | B | NA |\n",
       "\n"
      ],
      "text/plain": [
       "  individual group value\n",
       "1 NA         A     NA   \n",
       "2 NA         A     NA   \n",
       "3 NA         A     NA   \n",
       "4 NA         A     NA   \n",
       "5 NA         B     NA   \n",
       "6 NA         B     NA   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>individual</th><th scope=col>group</th><th scope=col>value</th><th scope=col>id</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Mister 1 </td><td>A</td><td>20</td><td>1</td></tr>\n",
       "\t<tr><td>Mister 10</td><td>A</td><td>20</td><td>2</td></tr>\n",
       "\t<tr><td>Mister 7 </td><td>A</td><td>24</td><td>3</td></tr>\n",
       "\t<tr><td>Mister 4 </td><td>A</td><td>30</td><td>4</td></tr>\n",
       "\t<tr><td>Mister 3 </td><td>A</td><td>49</td><td>5</td></tr>\n",
       "\t<tr><td>Mister 8 </td><td>A</td><td>64</td><td>6</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       " individual & group & value & id\\\\\n",
       " <fct> & <fct> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t Mister 1  & A & 20 & 1\\\\\n",
       "\t Mister 10 & A & 20 & 2\\\\\n",
       "\t Mister 7  & A & 24 & 3\\\\\n",
       "\t Mister 4  & A & 30 & 4\\\\\n",
       "\t Mister 3  & A & 49 & 5\\\\\n",
       "\t Mister 8  & A & 64 & 6\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| individual &lt;fct&gt; | group &lt;fct&gt; | value &lt;int&gt; | id &lt;int&gt; |\n",
       "|---|---|---|---|\n",
       "| Mister 1  | A | 20 | 1 |\n",
       "| Mister 10 | A | 20 | 2 |\n",
       "| Mister 7  | A | 24 | 3 |\n",
       "| Mister 4  | A | 30 | 4 |\n",
       "| Mister 3  | A | 49 | 5 |\n",
       "| Mister 8  | A | 64 | 6 |\n",
       "\n"
      ],
      "text/plain": [
       "  individual group value id\n",
       "1 Mister 1   A     20    1 \n",
       "2 Mister 10  A     20    2 \n",
       "3 Mister 7   A     24    3 \n",
       "4 Mister 4   A     30    4 \n",
       "5 Mister 3   A     49    5 \n",
       "6 Mister 8   A     64    6 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 16 rows containing missing values (position_stack).\"\n",
      "Warning message:\n",
      "\"Removed 16 rows containing missing values (geom_text).\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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JMZiD0MmIeEXyP7qdHwqquAFI/c5UCq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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# library\n",
    "library(tidyverse)\n",
    " \n",
    "# Create dataset\n",
    "# 创建数据集\n",
    "data <- data.frame(\n",
    "  individual=paste( \"Mister \", seq(1,60), sep=\"\"),\n",
    "  group=c( rep('A', 10), rep('B', 30), rep('C', 14), rep('D', 6)) ,\n",
    "  value=sample( seq(10,100), 60, replace=T)\n",
    ")\n",
    "\n",
    "# Set a number of 'empty bar' to add at the end of each group\n",
    "# 在原始数据中添加空白数据\n",
    "# empty_bar 表示组之间的空白距离\n",
    "empty_bar <- 4\n",
    "# 每一组之间4个空白\n",
    "to_add <- data.frame( matrix(NA, empty_bar*nlevels(data$group), ncol(data)) )\n",
    "colnames(to_add) <- colnames(data)\n",
    "# 为每个空白值提供组信息，rep函数的意思就是复制值，levels(data$group)为复制的对象，each为复制的次数\n",
    "to_add$group <- rep(levels(data$group), each=empty_bar)\n",
    "head(to_add)\n",
    "\n",
    "colnames(to_add) <- colnames(data)\n",
    "to_add$group <- rep(levels(data$group), each=empty_bar)\n",
    "data <- rbind(data, to_add)\n",
    "# 管道操作类似 data<-arrange(data,data$group)\n",
    "data <- data %>% arrange(group, value)\n",
    "# 设置id\n",
    "data$id <- seq(1, nrow(data))\n",
    "head(data)\n",
    "\n",
    "# Get the name and the y position of each label\n",
    "# 设定角度值\n",
    "label_data <- data\n",
    "number_of_bar <- nrow(label_data)\n",
    "angle <- 90 - 360 * (label_data$id-0.5) /number_of_bar    \n",
    "label_data$hjust <- ifelse( angle < -90, 1, 0)\n",
    "label_data$angle <- ifelse(angle < -90, angle+180, angle)\n",
    " \n",
    "# Make the plot\n",
    "# fill 按组填充颜色\n",
    "p <- ggplot(data, aes(x=as.factor(id), y=value, fill=group)) +   \n",
    "  geom_bar(stat=\"identity\", alpha=0.5) +\n",
    "  ylim(-100,120) +\n",
    "  theme_minimal() +\n",
    "  theme(\n",
    "    legend.position = \"none\",\n",
    "    axis.text = element_blank(),\n",
    "    axis.title = element_blank(),\n",
    "    panel.grid = element_blank(),\n",
    "    plot.margin = unit(rep(-1,4), \"cm\") \n",
    "  ) +\n",
    "  coord_polar() + \n",
    "  geom_text(data=label_data, aes(x=id, y=value+10, label=individual, hjust=hjust), color=\"black\", fontface=\"bold\",alpha=0.6, size=2.5, angle= label_data$angle, inherit.aes = FALSE ) \n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4 环状条形图自定义 Circular barchart customization\n",
    "最后是，在图表中添加一些自定义项是非常明智的。这里我们添加组名（A、B、C和D），并添加一个刻度来帮助比较条形图的大小。代码有点长，但结果看来是值得的！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**首先准备数据**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>individual</th><th scope=col>group</th><th scope=col>value</th><th scope=col>id</th><th scope=col>hjust</th><th scope=col>angle</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Mister 1</td><td>A</td><td>38</td><td>1</td><td>0</td><td>87.5</td></tr>\n",
       "\t<tr><td>Mister 2</td><td>A</td><td>24</td><td>2</td><td>0</td><td>82.5</td></tr>\n",
       "\t<tr><td>Mister 3</td><td>A</td><td>72</td><td>3</td><td>0</td><td>77.5</td></tr>\n",
       "\t<tr><td>Mister 4</td><td>A</td><td>47</td><td>4</td><td>0</td><td>72.5</td></tr>\n",
       "\t<tr><td>Mister 5</td><td>A</td><td>96</td><td>5</td><td>0</td><td>67.5</td></tr>\n",
       "\t<tr><td>Mister 6</td><td>A</td><td>86</td><td>6</td><td>0</td><td>62.5</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 6\n",
       "\\begin{tabular}{r|llllll}\n",
       " individual & group & value & id & hjust & angle\\\\\n",
       " <fct> & <fct> & <int> & <int> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Mister 1 & A & 38 & 1 & 0 & 87.5\\\\\n",
       "\t Mister 2 & A & 24 & 2 & 0 & 82.5\\\\\n",
       "\t Mister 3 & A & 72 & 3 & 0 & 77.5\\\\\n",
       "\t Mister 4 & A & 47 & 4 & 0 & 72.5\\\\\n",
       "\t Mister 5 & A & 96 & 5 & 0 & 67.5\\\\\n",
       "\t Mister 6 & A & 86 & 6 & 0 & 62.5\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 6\n",
       "\n",
       "| individual &lt;fct&gt; | group &lt;fct&gt; | value &lt;int&gt; | id &lt;int&gt; | hjust &lt;dbl&gt; | angle &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Mister 1 | A | 38 | 1 | 0 | 87.5 |\n",
       "| Mister 2 | A | 24 | 2 | 0 | 82.5 |\n",
       "| Mister 3 | A | 72 | 3 | 0 | 77.5 |\n",
       "| Mister 4 | A | 47 | 4 | 0 | 72.5 |\n",
       "| Mister 5 | A | 96 | 5 | 0 | 67.5 |\n",
       "| Mister 6 | A | 86 | 6 | 0 | 62.5 |\n",
       "\n"
      ],
      "text/plain": [
       "  individual group value id hjust angle\n",
       "1 Mister 1   A     38    1  0     87.5 \n",
       "2 Mister 2   A     24    2  0     82.5 \n",
       "3 Mister 3   A     72    3  0     77.5 \n",
       "4 Mister 4   A     47    4  0     72.5 \n",
       "5 Mister 5   A     96    5  0     67.5 \n",
       "6 Mister 6   A     86    6  0     62.5 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# library\n",
    "library(tidyverse)\n",
    " \n",
    "# Create dataset\n",
    "data <- data.frame(\n",
    "  individual=paste( \"Mister \", seq(1,60), sep=\"\"),\n",
    "  group=c( rep('A', 10), rep('B', 30), rep('C', 14), rep('D', 6)) ,\n",
    "  value=sample( seq(10,100), 60, replace=T)\n",
    ")\n",
    " \n",
    "# Set a number of 'empty bar' to add at the end of each group\n",
    "empty_bar <- 3\n",
    "to_add <- data.frame( matrix(NA, empty_bar*nlevels(data$group), ncol(data)) )\n",
    "colnames(to_add) <- colnames(data)\n",
    "to_add$group <- rep(levels(data$group), each=empty_bar)\n",
    "data <- rbind(data, to_add)\n",
    "data <- data %>% arrange(group)\n",
    "data$id <- seq(1, nrow(data))\n",
    "\n",
    "# Get the name and the y position of each label\n",
    "label_data <- data\n",
    "number_of_bar <- nrow(label_data)\n",
    "angle <- 90 - 360 * (label_data$id-0.5) /number_of_bar   \n",
    "label_data$hjust <- ifelse( angle < -90, 1, 0)\n",
    "label_data$angle <- ifelse(angle < -90, angle+180, angle)\n",
    "head(label_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**然后设置abcd刻度线信息**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A rowwise_df: 4 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>group</th><th scope=col>start</th><th scope=col>end</th><th scope=col>title</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>A</td><td> 1</td><td>10</td><td> 5.5</td></tr>\n",
       "\t<tr><td>B</td><td>14</td><td>43</td><td>28.5</td></tr>\n",
       "\t<tr><td>C</td><td>47</td><td>60</td><td>53.5</td></tr>\n",
       "\t<tr><td>D</td><td>64</td><td>69</td><td>66.5</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A rowwise_df: 4 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       " group & start & end & title\\\\\n",
       " <fct> & <int> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t A &  1 & 10 &  5.5\\\\\n",
       "\t B & 14 & 43 & 28.5\\\\\n",
       "\t C & 47 & 60 & 53.5\\\\\n",
       "\t D & 64 & 69 & 66.5\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A rowwise_df: 4 × 4\n",
       "\n",
       "| group &lt;fct&gt; | start &lt;int&gt; | end &lt;dbl&gt; | title &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| A |  1 | 10 |  5.5 |\n",
       "| B | 14 | 43 | 28.5 |\n",
       "| C | 47 | 60 | 53.5 |\n",
       "| D | 64 | 69 | 66.5 |\n",
       "\n"
      ],
      "text/plain": [
       "  group start end title\n",
       "1 A      1    10   5.5 \n",
       "2 B     14    43  28.5 \n",
       "3 C     47    60  53.5 \n",
       "4 D     64    69  66.5 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prepare a data frame for base lines\n",
    "base_data <- data %>% \n",
    "  group_by(group) %>% \n",
    "  summarize(start=min(id), end=max(id) - empty_bar) %>% \n",
    "  rowwise() %>% \n",
    "  mutate(title=mean(c(start, end)))\n",
    "head(base_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**接着设置各组之间的间隔条**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A rowwise_df: 3 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>group</th><th scope=col>start</th><th scope=col>end</th><th scope=col>title</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>B</td><td>13</td><td>11</td><td>28.5</td></tr>\n",
       "\t<tr><td>C</td><td>46</td><td>44</td><td>53.5</td></tr>\n",
       "\t<tr><td>D</td><td>63</td><td>61</td><td>66.5</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A rowwise_df: 3 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       " group & start & end & title\\\\\n",
       " <fct> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t B & 13 & 11 & 28.5\\\\\n",
       "\t C & 46 & 44 & 53.5\\\\\n",
       "\t D & 63 & 61 & 66.5\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A rowwise_df: 3 × 4\n",
       "\n",
       "| group &lt;fct&gt; | start &lt;dbl&gt; | end &lt;dbl&gt; | title &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| B | 13 | 11 | 28.5 |\n",
       "| C | 46 | 44 | 53.5 |\n",
       "| D | 63 | 61 | 66.5 |\n",
       "\n"
      ],
      "text/plain": [
       "  group start end title\n",
       "1 B     13    11  28.5 \n",
       "2 C     46    44  53.5 \n",
       "3 D     63    61  66.5 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prepare a data frame for grid (scales)\n",
    "grid_data <- base_data\n",
    "grid_data$end <- grid_data$end[ c( nrow(grid_data), 1:nrow(grid_data)-1)] + 1\n",
    "grid_data$start <- grid_data$start - 1\n",
    "grid_data <- grid_data[-1,]\n",
    "grid_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**最后就是绘图**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 12 rows containing missing values (position_stack).\"\n",
      "Warning message:\n",
      "\"Removed 12 rows containing missing values (geom_text).\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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JLI3AoLH591laGangtSokGK4EgzjP3HqMK0Oh/RYTfrvCAVbB9pn3PnIQm70yVI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NK5Olla1KSeNIyNR0ilhr84+ahOdhljkv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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make the plot\n",
    "p <- ggplot(data, aes(x=as.factor(id), y=value, fill=group)) +   \n",
    "    # 添加条形图\n",
    "    geom_bar(aes(x=as.factor(id), y=value, fill=group), stat=\"identity\", alpha=0.5) +\n",
    "\n",
    "    # 添加各组之间的线条，可以注释\n",
    "    geom_segment(data=grid_data, aes(x = end, y = 80, xend = start, yend = 80), colour = \"grey\", alpha=1, size=0.3 , inherit.aes = FALSE ) +\n",
    "    geom_segment(data=grid_data, aes(x = end, y = 60, xend = start, yend = 60), colour = \"grey\", alpha=1, size=0.3 , inherit.aes = FALSE ) +\n",
    "    geom_segment(data=grid_data, aes(x = end, y = 40, xend = start, yend = 40), colour = \"grey\", alpha=1, size=0.3 , inherit.aes = FALSE ) +\n",
    "    geom_segment(data=grid_data, aes(x = end, y = 20, xend = start, yend = 20), colour = \"grey\", alpha=1, size=0.3 , inherit.aes = FALSE ) +\n",
    "\n",
    "    # Add text showing the value of each 100/75/50/25 lines，设置值坐标，可以注释\n",
    "    annotate(\"text\", x = rep(max(data$id),4), y = c(20, 40, 60, 80), label = c(\"20\", \"40\", \"60\", \"80\") , color=\"grey\", size=3 , angle=0, fontface=\"bold\", hjust=1) +\n",
    "    \n",
    "    # 和前面一样\n",
    "    ylim(-100,120) +\n",
    "    theme_minimal() +\n",
    "    theme(\n",
    "        legend.position = \"none\",\n",
    "        axis.text = element_blank(),\n",
    "        axis.title = element_blank(),\n",
    "        panel.grid = element_blank(),\n",
    "        plot.margin = unit(rep(-1,4), \"cm\") \n",
    "    ) +\n",
    "    coord_polar() + \n",
    "    geom_text(data=label_data, aes(x=id, y=value+10, label=individual, hjust=hjust), color=\"black\", fontface=\"bold\",alpha=0.6, size=2.5, angle= label_data$angle, inherit.aes = FALSE ) +\n",
    "\n",
    "    # Add base line information\n",
    "    # 添加下划线\n",
    "    geom_segment(data=base_data, aes(x = start, y = -5, xend = end, yend = -5), colour = \"black\", alpha=0.8, size=0.6 , inherit.aes = FALSE )  +\n",
    "    # 添加各组的名字\n",
    "    geom_text(data=base_data, aes(x = title, y = -18, label=group), hjust=c(1,1,0,0), colour = \"black\", alpha=0.8, size=4, fontface=\"bold\", inherit.aes = FALSE)\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 堆积环状条形图 Circular stacked barplot\n",
    "本节旨在教你如何制作分组堆积的环状条形图。我强烈建议在深入研究这个代码之前先阅读前面的代码。本节会用到gather函数来处理数据，gather函数类似excel中的透视表，将数据压平。具体使用见\n",
    "[R语言 tidyr包的三个重要函数：gather，spread，separate的用法和举例](https://blog.csdn.net/six66667/article/details/84888644)\n",
    "\n",
    "该段代码和前面不同在于数据创建以及创建各组之间的间距条"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**首先创建数据集**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>individual</th><th scope=col>group</th><th scope=col>value1</th><th scope=col>value2</th><th scope=col>value3</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Mister 1</td><td>A</td><td>44</td><td>70</td><td>62</td></tr>\n",
       "\t<tr><td>Mister 2</td><td>A</td><td>86</td><td>75</td><td>31</td></tr>\n",
       "\t<tr><td>Mister 3</td><td>A</td><td>18</td><td>56</td><td>61</td></tr>\n",
       "\t<tr><td>Mister 4</td><td>A</td><td>20</td><td>64</td><td>99</td></tr>\n",
       "\t<tr><td>Mister 5</td><td>A</td><td>62</td><td>66</td><td>44</td></tr>\n",
       "\t<tr><td>Mister 6</td><td>A</td><td>43</td><td>50</td><td>31</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       " individual & group & value1 & value2 & value3\\\\\n",
       " <fct> & <fct> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t Mister 1 & A & 44 & 70 & 62\\\\\n",
       "\t Mister 2 & A & 86 & 75 & 31\\\\\n",
       "\t Mister 3 & A & 18 & 56 & 61\\\\\n",
       "\t Mister 4 & A & 20 & 64 & 99\\\\\n",
       "\t Mister 5 & A & 62 & 66 & 44\\\\\n",
       "\t Mister 6 & A & 43 & 50 & 31\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 5\n",
       "\n",
       "| individual &lt;fct&gt; | group &lt;fct&gt; | value1 &lt;int&gt; | value2 &lt;int&gt; | value3 &lt;int&gt; |\n",
       "|---|---|---|---|---|\n",
       "| Mister 1 | A | 44 | 70 | 62 |\n",
       "| Mister 2 | A | 86 | 75 | 31 |\n",
       "| Mister 3 | A | 18 | 56 | 61 |\n",
       "| Mister 4 | A | 20 | 64 | 99 |\n",
       "| Mister 5 | A | 62 | 66 | 44 |\n",
       "| Mister 6 | A | 43 | 50 | 31 |\n",
       "\n"
      ],
      "text/plain": [
       "  individual group value1 value2 value3\n",
       "1 Mister 1   A     44     70     62    \n",
       "2 Mister 2   A     86     75     31    \n",
       "3 Mister 3   A     18     56     61    \n",
       "4 Mister 4   A     20     64     99    \n",
       "5 Mister 5   A     62     66     44    \n",
       "6 Mister 6   A     43     50     31    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# library\n",
    "library(tidyverse)\n",
    "library(viridis)\n",
    " \n",
    "# Create dataset\n",
    "# 创建数据集\n",
    "data <- data.frame(\n",
    "    individual=paste( \"Mister \", seq(1,60), sep=\"\"),\n",
    "    group=c( rep('A', 10), rep('B', 30), rep('C', 14), rep('D', 6)) ,\n",
    "    value1=sample( seq(10,100), 60, replace=T),\n",
    "    value2=sample( seq(10,100), 60, replace=T),\n",
    "    value3=sample( seq(10,100), 60, replace=T)\n",
    ")\n",
    "head(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**转换数据**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>individual</th><th scope=col>group</th><th scope=col>observation</th><th scope=col>value</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Mister 1</td><td>A</td><td>value1</td><td>44</td></tr>\n",
       "\t<tr><td>Mister 2</td><td>A</td><td>value1</td><td>86</td></tr>\n",
       "\t<tr><td>Mister 3</td><td>A</td><td>value1</td><td>18</td></tr>\n",
       "\t<tr><td>Mister 4</td><td>A</td><td>value1</td><td>20</td></tr>\n",
       "\t<tr><td>Mister 5</td><td>A</td><td>value1</td><td>62</td></tr>\n",
       "\t<tr><td>Mister 6</td><td>A</td><td>value1</td><td>43</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       " individual & group & observation & value\\\\\n",
       " <fct> & <fct> & <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t Mister 1 & A & value1 & 44\\\\\n",
       "\t Mister 2 & A & value1 & 86\\\\\n",
       "\t Mister 3 & A & value1 & 18\\\\\n",
       "\t Mister 4 & A & value1 & 20\\\\\n",
       "\t Mister 5 & A & value1 & 62\\\\\n",
       "\t Mister 6 & A & value1 & 43\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| individual &lt;fct&gt; | group &lt;fct&gt; | observation &lt;chr&gt; | value &lt;int&gt; |\n",
       "|---|---|---|---|\n",
       "| Mister 1 | A | value1 | 44 |\n",
       "| Mister 2 | A | value1 | 86 |\n",
       "| Mister 3 | A | value1 | 18 |\n",
       "| Mister 4 | A | value1 | 20 |\n",
       "| Mister 5 | A | value1 | 62 |\n",
       "| Mister 6 | A | value1 | 43 |\n",
       "\n"
      ],
      "text/plain": [
       "  individual group observation value\n",
       "1 Mister 1   A     value1      44   \n",
       "2 Mister 2   A     value1      86   \n",
       "3 Mister 3   A     value1      18   \n",
       "4 Mister 4   A     value1      20   \n",
       "5 Mister 5   A     value1      62   \n",
       "6 Mister 6   A     value1      43   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>180</li>\n",
       "\t<li>4</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 180\n",
       "\\item 4\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 180\n",
       "2. 4\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 180   4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Transform data in a tidy format (long format)\n",
    "# key表示观察的变量就是value1,value2,value3;value代表值,-c(1,2)表示不对第一列和第二列进行转换\n",
    "data <- data %>% gather(key = \"observation\", value=\"value\", -c(1,2)) \n",
    "head(data)\n",
    "dim(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**设置一系列绘图指标**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Factor `individual` contains implicit NA, consider using `forcats::fct_explicit_na`\"\n"
     ]
    }
   ],
   "source": [
    "# Set a number of 'empty bar' to add at the end of each group\n",
    "empty_bar <- 2\n",
    "nObsType <- nlevels(as.factor(data$observation))\n",
    "to_add <- data.frame( matrix(NA, empty_bar*nlevels(data$group)*nObsType, ncol(data)) )\n",
    "colnames(to_add) <- colnames(data)\n",
    "to_add$group <- rep(levels(data$group), each=empty_bar*nObsType )\n",
    "data <- rbind(data, to_add)\n",
    "data <- data %>% arrange(group, individual)\n",
    "data$id <- rep( seq(1, nrow(data)/nObsType) , each=nObsType)\n",
    " \n",
    "# Get the name and the y position of each label\n",
    "label_data <- data %>% group_by(id, individual) %>% summarize(tot=sum(value))\n",
    "number_of_bar <- nrow(label_data)\n",
    "angle <- 90 - 360 * (label_data$id-0.5) /number_of_bar     \n",
    "label_data$hjust <- ifelse( angle < -90, 1, 0)\n",
    "label_data$angle <- ifelse(angle < -90, angle+180, angle)\n",
    " \n",
    "# prepare a data frame for base lines\n",
    "base_data <- data %>% \n",
    "  group_by(group) %>% \n",
    "  summarize(start=min(id), end=max(id) - empty_bar) %>% \n",
    "  rowwise() %>% \n",
    "  mutate(title=mean(c(start, end)))\n",
    " \n",
    "# prepare a data frame for grid (scales)\n",
    "grid_data <- base_data\n",
    "grid_data$end <- grid_data$end[ c( nrow(grid_data), 1:nrow(grid_data)-1)] + 1\n",
    "grid_data$start <- grid_data$start - 1\n",
    "grid_data <- grid_data[-1,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**绘图**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"Removed 24 rows containing missing values (position_stack).\"\n",
      "Warning message:\n",
      "\"Removed 9 rows containing missing values (geom_text).\"\n",
      "Warning message:\n",
      "\"Removed 24 rows containing missing values (position_stack).\"\n",
      "Warning message:\n",
      "\"Removed 9 rows containing missing values (geom_text).\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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RJk5GwuOqV7IZAwTU0OzljEKMMHa250KqbSh4Wt2DtaPWeH3B7JCdK+fh95kWeB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rmpkYgIljbGZxn4YgIR+QjssdQFqK7UKO\ngiS80blesAKrWOBYRrjEC0+NchKlZKZNv+yqZiadh6M5HOJkco4e1u6n+IAUKl9O8jWES1jP\niABJ1KJsKM8+YxBvKrt8m86+BnLhuWuUkzBpjU024Zdd1czGppIV/CrGNos3TkNn93HPAEmd\n9gPSsg0eAFJYp9nTxWTdipbLKspCP43+GuWzEmHTGGlPNTOPnIdO+HHPBEmcTv79xlkhlWxD\nVh8HUkBPOtbhhpoH1SifQepT6Ww4tZrZ6QS5jnuEJKsWWn7zAckesvo4kE7rVmFiKLmkDWv6\nL7eoZnbSrfbra78pSPIkBrQ+INmmY+XP53MUU93l/lSR9F2teujzdpEaDM104ci4EUjq+DY7\n7wOSy9egNno8SA/E6tm6SM9LGvHJcZpO0ukgzYc29xEF0rM1073FNx2rNnwiju7L07P1kEyg\nA078EoI2Uv3X80DSDmrvHqqPfEAKS9hAGz8ZSCQ3b6FbnyBOGhl5JIBK82rQft7bimaE/B3j\nA5KQqMwnYp8PSA+UTDgQh0QkIFvK3lZcIRTaI1S/eHeQfNOxdpp283QmRCA3b6JbnyBKGhGs\nJOa/hm4F0wmtGAeRAum52unesjOn3XyAD0j3FVoUMgqQIEQjO5xWZd47niHVHT4gjTvbbz7I\nB6QzJGhyt8EsSZDhCEASqxb1Ka57M/QBCWWHZadEcCGO9AHpoHBLVsuW6QFIWZKJlVa4aiRv\n2pXb7t4MqRfqu4Mk/tgP0gKnAJ7O5uhFQGJ6Pkn1ZYr58eaQ2AbUT46x6z2tKsRIdH2Fb1yD\nHWPouzzE+O6DJKmQxjG+TbeMqMM+SCGlrOTteLac1j+GrubARFMZgly15YVNyYpafpkneuaU\nDLQPU+t8MdfEal1hIEEn5CvUDgJ/vjNIumU3jnFta+FkPvjdQRq0AcNwXiHgs/pHK90ExmUX\nwEsH195naj0uLvvI205PNMnV/kzYdDwIpOXZToFoXH5+b5Dg30XrHAVpCZOBptuB1GkBMzjb\nElY4wttIR3aetE82L7YltVNv4837Uq7BBUsN1RDknmSruSJQSENX4FoqPIzII+EBaXGqwwxt\nKfr+9radLfL7IEgrnu4FUqNWCIm3P078s9Ke4zuskY7sTPTgFVT4ZwvBcg3pmKEuGSvlenfS\nVxNRkGUiRzqqJXIqQSwOj0ABw+pDppbiotyWIRNE8uv3BcnssxM/HgdppZ5uDhLHNzW+nQtl\n5SWG1CaRrXRk53qGm60jTGXQKYyH4P8M+Eqp/suA6ma1VqrTOMp63J/xYqW1TATJZ3EGRfaf\n3lcl2SeRxAYncLSi6fw8VPOhqaQLDMAHWkgunMRbb1hcKx3ZuZOaBpWMyLrCUmAcCMhZJkgX\n9l6PH7uW43tgtXoXHXU54/LlIKaR7JVm1o/hRhDJnz8g2VrG/uue9X76qW/AEXqzqOdVNMwA\ngqY+ebSVjuw80IhHKB0xnMlFIvEMLpBVqE+0/ERSKoHVLDWVfMqkSwV1WmouPW16Aococh8A\nW+qtQfK1n2WLHSCtYToFJ+1oMAJhiA9MrVDHNGujtoig61jvIHwoWX/eqKz7BSCTar56kXmP\nPI85q3gnCl0Y/PlMKaGhbQyGq7X1D0HkOcD4vrZdUHgQbXoaSKfTpB0IuiITVZLA/qEKfuk6\nf/227JenmSKbdXOuGpP1Q7UMoXa4zPAKgxwm8iGLMZ2WvHKc1x4tpWLMNl3maflDFHlBeleV\nRHftaUnV8c/j6FSY5mNgL62gz0LdsUpNtgBIkHaBVd28YYQn71jnAO1Rw7V0MD4itUMGaKVs\nOJkqDJgpxDRrjsOplUfOKSGtfgQizwGwqd6SpLB41WW/Pw+ks2Ca90e7CbKa9vAH07MTy7J9\n5Bo3zOM42+lAGyMuDPgZMN8qmWpSMUlnAaoiRs5s+K3oYWuAvQorehvc4scochxAttVbgiT/\nCAFJ6/fngbS1zsYD4ctoME2QVCVooUyN8Dv0PXD0E6sU+gUP8uXB4P5Y30ATE64Iy48R3rMP\noeswY15BG6ZtNcyKCjRpU3BP9qK4Fj8Ekf0AKszsHUFSCmmMyLMqd96DzVZsSIwROM27lGII\nT/662futCr3UKlmq8EgPFUuZWKhttPWw9uuxvoHaBxwLmEm50IugjeQoKOm7RuommsPN/Om/\ndrR4GESuIxv3UHu+JUj476Ip/CDpXXfHczQczcoFSgRHhEsnBh00oSkqgoEugvgGrB0xthjM\nls7ZjBuxd9cW5cIJAX27OAhSjxdC8XOdDOfJRL1bLHAhpmBpG6oDwxvPGG53kx+CyHSAVYTZ\nG5K0GCHpf/tA0vrv7ue5OJiPpsCA5hSREGjM/rFMTs7kzbwhHwcseJ5ifn3oxwmVJte8EFTc\nnB8NWkWAaqEEa3IloA+hVc4G4U/gFQ/wgRxr82MUrQ8wrgNXn2098R1k42oYA5zchj584KGG\nqhoS72aJLIEE2kb0WLHEoCHdkylHM6BT0IxorUoya31aBWFPTB7sGtnMt3IzkA9BaMQseE7r\nQFtLcUAUeHhHh4GDvJ1KMvi+1589IKl+vPehOkEy0uTaQvTRPFktNaBpJC6Nq1FVFpNGXQq2\nF3yZtl2hlyZPKHznYM9gKul+plL/cLECouNeI07I3jZey0GKvuqpV81HejOQzL7vzXchXZ8O\nuOOh+kEy0WT9seUYx0aDI0EQWnGcw5wnBD0oB7P4L+d1N8DoCO2sxfC+SoqOtjzYM9AHgkMl\njA5i4C7svc44TXa0rEMOQSQOgDsZkHxHlWSbjF1+vWpDX0ePe6SBHJlgGs1BziB9x8HtPVeL\nRVeZDAkXlZTTcVkHttvWpABFUZ8BktCNJYtgh0TefFyzeuQYRV+Ns/NvrJK+OKIatF9WbRjS\nycOfSAxIVp5sMvE0SCsO5zi7DoYkoKUqxVPe8IIRSKVp5pMYONgxuhjtQ7K669iu7pRDELkx\nekeV5IkOkr+uWjG0c4c9kx0gRfNE5l4tNU+K4dbCASFicSBAfDDH4hRngBQphtuN7+4OuSFF\nb6iSXApJa5FVO8b06oDHsh+kSJpAK9D0Eg7sQQdVXIZbj7X0OGydZ+QnuFu/sN1pfI/3wbBr\n3CV39IP0PiQFhaviNnpLejt0XutxN57ncpCjWJq6rhMXV9OULSYPaToYHNWWhbT00z26hfsu\n93R7OwuHKPrqnYoa30glza+MKJZ8XRkCAfLFKjPXozkFpDiYhIArXKyvo5laW0A40XXjbhFw\nh3t6vhmFwxSBWDvLu6mk2bAbY1DyduNCzDouxfZ0TgNpfRnhUqZyDtZYkVksU79drwi9uT2d\n34DCTk/6Zs//v70v6bVVV9I8pEq61FEyZZryrAoQEoUSWSLF4PL//1MREbYx4HADrHbvkN67\n+yx6Oz5H42h4GP37s6wkW7GbA1CyBjLIwJkFpE1stXN+bsOQ810iSaqacRN5JbbU09c8hCuS\nPuwU/2+BcHY7ynkpD6MfJZJ2nobZC6V1OIPM21tA6rKtvXScxhuAE3ify6SSbG9nivSvOgcC\nAwR65MmrHT/zMPpRIungaZh5KG3H1M+4FWYwYOjLlCvbw6btZFwEDEd3DpTawL2TKU5+1QkM\n/LMB0Zmb4MXOIx4Y/SCR5HJ97//tANI/TizZN86w4ghua+IuJ8bj7ARTwHNxne4cKfJ+3wWk\nK1+VioEDiJJvglezR3kY/SCR5HZ9O37a40iPL8O3oNn1BKROB05jCNvBG4Z3usJXHrp3rNBj\nfhdPXPiqyyBKvAte7znugdG/PyW8gd2Ldf3IjLGTb0GzkwgkUOwKspZqEz6wowtc5aVHjNhN\n97nyWddRFH8TvD5wjmfx/SHKXSDILoijLZbsO4NmJ3XpG8iXA81u6spjNSxNVziLo0cM2U33\nufJZl0EUexO6Q/A0D4z+/RnKnfo8fgQigKSG++9Bs5s6sCqgmo8kzS5YYOQKcznpcUN2ma58\n1nUUxdyF7hDxLDyTg9GPEEnBIDv7YMS0rQSaHRbDwrC1Tmt2Vbjx1xUG29PNw4V0G0Nc+K7r\nIAreRd0jBkZwLs9CpjTXXeP2hhQTG7QejJs9RRiXBkBCRCnNLre7zXnoAott6c7B0vQOQPJh\nIP7uwZtEoSgMo+/3NxjFbo6AUtSIakLNDrNBwU83bXx2YyOacErOFTYztN6uKur+YhuK7aBd\npysfFpiBW+5yG4xWqfSlZCt2s2c86KTYYUVCOWRaAHW2z05FPNQrW0umTsEVTiNa71XQm9wx\nanfcBOjKh92AIuYuaSHhc7DrwspC30sbA2kOrCyRI6vmgUQQAqnQhauVZgfOcGDrnODTF76C\nwVeY7a8FpClbgeRq2JA8ajfQlQ/jQHQ1aE/d5WYYfTmS9p6GOSSk44dXFqaETyaRiwet2UkM\nG5p0cXhtPvF0hd/MTfoVSNiiqDnf//I8kMau3piIV77sOor2d0kOZ53jYfTVSDp67Hb/PA+l\nZYxH1WcSZBDVBlaancxMOftFMtTdvlyPk05y23qDBnv2IZCow+SzgSTbKs9229Env2oDgWt3\nc8Dob+Qu1RxupXX44SvJ6fk+/HAcnfjlCg0TQNBGs6M23YtkautRaVxRToAzfLJeTX0mAUhU\nZhWBXdRhZ7xj4E5co6zF3bee+SQbAtfvtEfRvP31NIxcjHRm2D6A/jBbSI6fTkIJBntshnmr\n2S2/5UpQzbrbeEC3WymVT9YrKYJWFbrLrYrbJ0Yu4dypFZQijEDKq3Zjnp1l/+2n3SDY9jeK\nWSUTYfS1SEqKsbsApXmn2QGJNali1Mnd89S3MfLhHLcBYCnwb/n/QlhF7k6MXMK5tV4klkce\nzbLT7H/DLRT943T2XYMRx0Inxvr9KSnG7iqUxkapdM3Cx7lQtbcxwQJNpKrT/eri2tGl81tL\nfSapxEmvESRPucNTgJSrhCzYDnAsE2dY/4ZbOO62+/kBMPpWHGl2OIOkJCjpFQ+q0I+6vQr6\n8+Z59dlVyoQo92/KUCLPgQxCIA1QOp+ePZz0gicAqTdWEcR4yEqIbcfx03x/4Rau2x0OnIeR\nO5Pi23HEfHbYlkyDksLSCAJgYelyMvUPVMgDCqZKDsorEelRS2A7kEEIJOgF0ZqiQd3xpkMd\nElMJQCoz7ZNs0TIDt539cae4PnUUQjd0HjsPox8kjjaOBrIkYGkAACAASURBVC7rKKqeUCqU\nFppgL1aInISPbt+qzH/47wz8V92BpfU03SZPYDlVQa4OcoHM0GN8dd/lYWd8PJAmRA+qq2tl\n8XMO8JNDEL4hc/heGH0pjrYG0rz/+HXcboWSwRLt41Bgg9LsQD5hh3th9m9FNJhYVlrPoLIl\nyzOwOVG+6YCs+JsqfY8RO0zxQFrsvlYhR9Wd7LKtGEzi+bSvj7klf4J79oIM4eGULyR/2SB7\n7AIDdxJKUyeEwFA7rdkRtvJ6KNd2dodcWi+D+xmwQhmkjLDRIAhkImzTDo0qESkq7RzwDV/c\nKOO9q0mJ3FY1CBTb4vxXURR3D/c9/ec8CEb/J3rw3p+OO0j239bowYhHICmpQtr2VbRmt7BX\n1aiWeBItC+hVvNWxyqwIuMd5HiQZpG7fEYKE6YM80hkj+eLzUGR6NJB6+IRi667TX8y8ciqI\nwvc4haK/jpiHMCdE4OibkOTaiXVm76lhDwxgqlTaYAkUHtijpI2dEbFEsXDwL+vEsc0zpwN5\nT042VDJIaXCVibPo6T/58uy+bUfVltnRz2U7gJ5jy2uuMKxAulXbFKx9CdcTKBqb7f51Ooyi\nzkyCES+ODmrdtyDJHdFgftgIpDgkpUPJYGlUHu9a7yCNTY2L9sYzPQlyeAWVLiIHkJpSt4WV\nuonrqBzT6KHOlacOMFzgZm0vsrJ2+8c5ILVV1dsm0KTjDIsanfqDetzGw58IIvWO25iqB6Bo\nBRJeE8EBkTCaPwFJfxT95//znoP/ZYbigKNHQUlhSZKCJUnc4L4taHZbmaBtJrVb21dCNIHw\nPCc3krBRjNhpG4xaIJNXutfyotw8cTeE8H/1Qf8jEFprwCJuR9kKvNE4NznV9duFJSWiaD76\nK2KhkXi6Fc13HkZuL8PbI+mPIRZJHI7UaNicvpmCR0CJZmnCWnHgbSghxLU3DoiVFuuoboxm\np3g8tesdkOxGBAuItopaucLTUW1EJ2JjFbREFz0ie5e/9Aff12AMcI2eE0CRrXQV2Uo9NXMW\n+V6upoFophffbVunoCjudAUkPP0CjDhv3bsjaQXS/+LP0H+6vtqNo79RQiklyWInloCUF3oR\nDDtzXJEJ1BOaN88EbgPJugB2L3Bdb4j3WyV9yjVAvKfoWrgCHBHd2q/yj/H4LTSuuK53Kmlp\nYFSrL1hF30pxKLLsIrxLkm63v1kkkPDcmGlPxdHbI0nB5L//cGUm7IAGt1By4yhOKJ2CksES\n+sRV22RHMLiWDpL4suIKTcbSRN46qUSRoIcqfFYKJ42xpkrLB/FH9aJFQQXVzQVpdP0OSDWE\ne3ey1Vf2oLMeYinCIJo3dhHFy2+jL5JglLJ5dQlGvt2j90bSCqT/7Ts+8yOwIsExsGEkXYMS\n0VGzAzKljpVkmrIrqXlwg6ZEHQs2juq2oYdqlxpm4NX9hNXEjImmReAfndNEroRSv9W4A5Ls\n8Q2l30sSRtG8sYuWVz2Uq02Ckf/8p8BofnMkGc3uP/6bOYz/9Q2Cr4nYc6AknSUcDO8UiqP2\nnr3T1CiYAB60xmYZNy3pZK1sjQPkj86iououg7piBPw5gvcgoOJq+SLLLiqz8hh+kYIi/vRE\nGNFcn8PRe5MB0n/+j/so/tf6Uh4I3AA/BkrbOR8dfgRTEE8D6IKRtKOxWdBQoK6kVDrcdhq7\nqqCHFDsv4h/K6y0kvZdUZ7Z6g2pPNwB+tYsmeFCxLw+YBCPu/DMw+odD0vVvfiGtzob/OE6e\nK56ByaHgW0Mwl1yFUmjerSYWBQFpb4/cQyidBPItcW1fTzq/1xrJRdXSHhHtmVuMoXH1qN9N\nll3Uw3c3EbqddVTWuQpQ8pyfDiM9yV+HI4OV/7vIJO7Y7mvdg+Ad5wgkJUIpvISuVgG52TBb\n/e7hm8nR1proIYoVGvaPwoFUQBZkTRHI5cGddhNZdhFGS4DIrHJP8Ov+auM6ZM7fXsiKGReM\n3I0vP5uMt+7otgvEMxx+fiqUOA5YySp1DDsxQhs1t9PQQsQSIELAVmou172ldSzh/1S9h1Z5\nEqQAPr9P39ySZRfhtu6Q7QeAh9GawNFz5x9gFELSeprz3EcMwVNJo6X+8+c/9gfS2omFhH8c\nkiKh5FlMFdntyUatTUUVET9Jfb6u4vtYBBXZQPCalMpV6f41j3gryy6CPjl1nnmAdLiaIFTs\nNhX8MPICyT7NdXL6B75bVfDVRvrzX/vf1Z+uQTniKICkKKHEdu4NAMkFJaPZDQCnoe3qq97v\nEE3tgh+yKw7W2B/QqVR8BOpNAk7OF6Vu6B7yUpZdtOYH7mQfuwpJk0HpBxL+6hEzNsvsZ/A0\njNTp7Mbni8hyNvzP9mf6wxmjO7vz+oKenSCO/omEkvv2GzKanVAKyt6R9kDah2sv46l3aRE1\nKn4hfyCuLbuIgFR2Lr/d7qqpAWzP0nQf7fcXeGDEAulw3u7kpA8zF/x5LyiZAKH/YnAUgyQz\nONeEkrlJEErsA1YaGxVYo3Sn5iE+OzfJptxZY390QmCBR7pyMdpc+0eXaKiEgaZtFwGKpvno\ntzuSUknHqpPoncnlxhHihxGHJMd5m5OTvtG65M2Q5KINjsJIsgbnilCy7+KHkvcJe4IKeb1s\nsucJJAf90Z01HuWkm6dxsJaKjV000BPHsLMlsyMq6Gq3cof/4rGx5RRmLk/AaFdf/82RdHQz\n+JG0GZ0LSNreZvZgyfuIA5Z0IOhDXHaR9Ie64UKselwZvkSSZVaWFpCcdlEXgrDcOOqWW+aZ\nM8sd//aAY8sn7DRewNEnIMnlrvMhaTc64VToCBwFGu74n3DAEnq+XymPAEjE4vIO7dIRxUEJ\n753RxHrOLvLfGC4y6h/sdNU7D8U6uEz1rQOT+FSLCzh6e/Xuz06tCyLpODwnhZLjPrN7GoI4\n2gulsYOk8FfSnxv3imrLkdE3VYvgKXbO/Xi7yCLaht1gfdd4dFO+IQikOWDsJn74kQHfF0me\n3SM3ktzjc0IosQN9A5BeTzdOtuVLa3MTB9TtREe8XWSRyITYie7KJcrVKDvBEQ+jizjS0uwd\noWS/UxyS2BEKMHoUjhgofSCO7gQSbpPqksaZRpLe5d1T0C6yaZE+/c4bUh11w3WcvUCaH44j\nSyi9F5Rstc7x4kcpAqdygxRi9TgguaD0w4HUoEe7J+x0ZMZMjqoV6TTB/QodYdflautra9bZ\n4+ycsMfAiFnY6dh7IWlnHcUgCYfjHJJ2UAoO+Wfj6EYgTY32SreK5Yn13dn2STToSpuD6FUJ\nir2TZjfSLJDm5+DoDaH052AdhZU7NSDsSAXZPQ5IOyj9cCCRPlf1urDfUFNUuztLOPXWM6X6\nZoVK6PWEuXqQNN8NI4dZYbEl0Jvod38czrqgmWTG5CSSLCj5h30DpY/E0VkgTV25j3+gxp/w\nF/gFEEVFY5VET6QmX+VYhZF5CKR8mkcQR4WFTddYc3P1PBy9E5RcMAojyRoV52gBR0ewfByQ\nVij9GCABOCpbIvS1AEE0qHpFylld6KZ+p3S7xr6oyMp6E85gW0fMYDsn6mYYudxcu38ivRpK\nzNZRAEmhkVMsHQWlCBzpCQjj6CuANLUFeLMtH5picbCKirUdB3bfaArtt0vS7aY2b3LbSaeU\nudzhMmcHOxlGexxFVDQJ4Wjd1n0llDaPDgHpX6cyNjsGz/B0BNvH4SgWSta3ifq127ArJc5u\nqTFTKvf1hCqcapykxUiuRAfVU4aQnqRtX8jqwDRETajU1VK6wnvjkARnpuEoAkkROFqx9Cok\n2RB2v6H7Iw6D48QRcXUE498JpfXjYI0WD0k/TabEySU7Bf+cdGeXCuq7IqxGJXvA71BJqdvK\njKnRsMVu76nN8ka1GD3GFcUgaU6H0RxG0iGC76A5bcTSK6C0hZHrFRkkOcbHiaO/twulIJSs\nz6uPES+vosipnTp6W6HDTaeSVK+t/aNTV3U47snEpm4HpEk5NvrKMWZhIMFZJ3AUQhJc58eR\nnVsxP1+/+3OAkVP5dCHJOUJuHN0vlAJQsr4wMw6ul1PMxE5dadVYzk0rpo7+UwqozpqDVQSi\naGhbE44bgSNLWk2NEALlXJ7i6AsgCU6JmjkH+ZBEV3pwdPCEPxdKGxjxPm9nlWJmkBggxSAp\nTSj5oGR9IvUyVjGbL5ZLMdMqTOAcanKmbn85W9XL8VdygcOXjW3bR2l0a2mtZt0fqlPWGT+Q\n4IyTMJp9SNIX8xx5TK6Y9+z9MNo9xg3v80hyMXcEkm6BkvWVOSaH0sZ8mYmLu5XXKGZOKfUB\nk9EXDu9Jt1N+u9FqU9EpXBUJNlFjgsBBmglVlpwL0HOTB0lwOGrKOOKQtF4eyaDmyj+Px5IH\nRnHKXQBJDHuHgZTeD9MHJGzgpRgFraVQF8xHUtSErq7nhduhNa7S7ajKY9u2nZTq16pMylSn\nkDn4flUAXXU3S+orcGkRDN3cjST7Dm4c7X+Y/92LpQdh6YiiE0hixs1TuRieFcTRmc6yLI7Q\nIm/JwlBr/QtlUtRsQspCjriBSvyd0e1sXm/ctcJDVGArqREdDBigJ/Du0jsq07Zig69a+FUc\nuZG0vYeLO48CSTMt0YOwtL8ti2kfsihWlUWSDyohHB3GLmZ6mBvhbkiFTrsxMzYFtfd6PkVN\nJeC9R4kBQJpW3Q60vakupxl6XeRnvqDVu7ktuLYniAPC0uUln6/UF3s/xmkYRQUzHJG0v0tE\nJPUaKWrodiwdbsi9SwhJGjLOAWOx4kWSb/hSoGTdBlfyQodhiiw7tft/F8XNI7wdehcGeNvS\nNIBudEGgs3IVu8zksIi0UC450/X3PPXQK4dnnZm4W3DkQJLjPgcc8Ync1o1uxNLxVh5U+5Gk\nv4kbshNICg1gNJTWexBkUH+BVbchPamJ7s58M8VNIrbWBOUOC8sp3U5VulQhQjGu7iNJsxFA\ngKwkNmlb/l0Ubk0Rm8LYrdKQXBN3E4zmA5Icdwrg6N89rCy6AUt/HID0PN4FJEfCwwkkcVA6\nvvJJKFl3gNZ3VOgQ/hrVrswsxSOrFvMUN4PY17kjJ4nQLS107WXZiDoy31Waj5yaAusW1Wbz\nNVfuvp4wwuBSUg1zlTRo6HHi6EiuOx2A5OVc2/EAdAVLLhAdPQzxImnzWXchKW4YI6BkXY9G\n+8IsNbqRlRO5f5nfLnL6UIzSbqyYt7pdPIGrXOHDCubWTotaaYfC67Wo1TP3ml8yjB6LI795\nv4t4APrjBESA3Ne4HukOXnK88e7D7kHS5vXM+peEpMN0tajJtLivubCEiqkpXuYCj5w41O3M\ndqvR7co0u0iLX2pWW66NdHU4Q1a3hd/xrUs1CA5I+K8TExNLMTiKEEjmyEp/LGIf/2dPu+Pc\nOwTjGZylt7iBDMbC8TgarA3CK1AqdKcH1PJVPPNjOo1FUSSQeo3/TCe9DknBqFOdo0Ohxs+s\nCVDqk7WDbtQFWN1aXZureFgdWO4s2YB/Pw5GrltHRIF7/rl/wAEpHtpf63lo0OU9O9OGbkGS\n/YqQLWA1T01Ckj1rqn0XhgPkCCjgIY+v99EUq0qQ2CiVa6Ask4oASRK/9PeMsmf5/3FoW8AG\nLSOwG1CtXrsDtSSpJCWgTNk+hugp4siJo2AUuBdXswtNPJzYN3PfOPqduD3YU0jaBoRvXhNF\nSL6qX9Eg2k/c1MCCiivvqJ3eSrmZ6lLUz9bwYoFUUXPB7MDAMaTXDdgngv9AmIRQ4ocOF4IE\nDC+X603UUHdcehKCUNK/QJHzXvD/Pp71s7RjgymZAnd2v1QcktgBjUbS5k27TLG+d0yj5w6d\nvp3y4BlzmzSbwuIl+fhN2lggkW43N9mhVkMECb3PpBKW6mylWmuMAZksMnuHoOI8EqlTkUau\nxFv1B8+iAVf45sx0ioZO0N9wN5Icba5Ascv3RnAshBzzB+U71nA1FaEJW0nYUVzpNn324P5j\nSNFeIvViadCeSMCu4e6C/kP5Srko1n8ylpEhoVBn3gb7Je0uSpyHVAK+YPkslmM9vO/w5vHk\nuJu3tEmMSEpEUoxQOkyiOPp6IxHkmsIR7tXqVR6XWnoA9KkEmTQ9qf9YNJCqxHzxGXvR0lLQ\nUwSH2nuC2wwt1tWXanVq2+C988wq49DjdVO1HaHUSUglYhCey+IY1sPe7D88Z90nkjjMXELS\nZgCB4VuzB9KctpTcHjztFAY9r0dYAcjyIntQM+QtRQMpOV98NJYR4KUStC5sl4dm15/cQ8uw\nrNfibhLmL1mKMFNg1TMBaWQ4hOWxKH69DKR/PULI9+SIuKDbkbQhUOzLQSvx27oBUfDhZrIv\nYIXt1EKLhnw7y7qoVFj4M4KGHpgXk6+mDzjjMpMmAQtSl1dyEAnfuIzNuksAMeN4+zUw6Sgt\nAqOfShaHWLfbPSBCIMVy/g1ASkCSFzNnkbSDEuwejjVZt2O5dwhEwYebTDlba3Rvdc+S5Ca8\nvQnlgR4IpNZ4E9DfvZG/2t0QsowMSTDRcJuglaY+V7nOg0Pt8o58Mm0YhGewULCQFzrXgXRS\nJNlD5B45fkijkdSihy2nfFBHM/sY8HgmVO3JFvVAd1e3Jk77aCCBE6UiH8WyLEgleZVhiAtF\nER8YIXGvCRYXGCHyVaxXO7SuwLCn0p5DOPZKE0jXgZSi223//c8akxGGDMvljqFhkDSimKBI\nyh4nUmXenN2e3U/p1AB2iMuE9kwBE9YdHOgjDPEL9MiUZwgPb1GVawBO+abNyzgMKT5JrKOv\nvH0Dpuyum0oOWREa9FQ6ckgcjpL2lDxyx3PROd3Oet39MLlHzzOscUgqEDcCOAHWVKyd281X\nYoYwx3A/UyV5lytar3HdBqR2+V6VvJkeCSTcaMZaKZhZX5vsixPGH9TRnzAVCYK/i8zKgzxI\nCma+LxDDIY7H7HGToNndAqQ4kbSOiquCnXsAPSPrRZJi9QZ1E4xIwbQzqe2YbcWoAHK2tBd6\nQJLWWKtWKUimTm9bPkwoPbQIB3rqCho1sYvjiKLVVQgJtDQUgMJ+FWZHScHM9gVi+MPxmH8Z\nzo1idB4Sp4HkFEl+eHjLNTBjG0YSzDp1/BGkdhVKsQPnrs3dPuDsaQtVRb3GjK6eLbW/ULYP\ndOA9FEjoogPLj2I3VASHYFL2DrSMsVmrlHeizbcOc8e0uaf6CsXjKFEgRUMiGkgRe0e7V46G\njBdJYfWuQh4utAOqUdJh4fJmV8LQB50N7Z5g2EasfgzlyRMKtfXj8tAfW2INPXVqiTB+legN\nKWF5ddAFuOhzWuclcs2ae6J3FFEhfyWGNxwP+vfIt9t/ewVS3J6SXwiFgBQDj8cgqRtMkZJe\nC6h5yoWDHTzgsenwCE1TV9cIGBWShvk/NaVZtOpV7qbHAok8daZCs7N2N0NDAbX0183bPBNo\nKRarz5uZNccsHykBSRdw9AIg+XU7B5LiIXMBSYrPa+1tLZSSpUXFnrwAUuR+xIaUi1hZSGXf\naH9XfK23WHpw0U/y1JW0DM19fKmk5duLzS6DTiL2i6MjkrgHRCOJ4QsXNx2AFPjh8UByyMTD\naztxweCFY2rvfKxsji7vkXxqA80yt7SGYOQqC3a4CShDRYtJbFOT6x0lyMhpy+SINz89GEgU\nR4i9X+IrpyKFLvHM2X6G3RSJpGgcpQukeGMnHkjhTdgYdHB8fAVJwOZjmRUjRVyCpTseIsFt\nSsaRA0o1PqkluGIdt0mH3Njs1Veivmg8PRhIq6cudQFoM28cnnfKYnAUSdx0uVjpAKTAD/dY\nPn5lzgHmw4snIIb7/W9s8z5T5wb+WyqjuR+cmsoNQJplVQwAXMLrIErl1hO5VdCNtievVXt4\ndD13VbOrj90Jw9go6Lhk1GgXBWZsDeHxUYxIuoSjJIGUZPlsj13U7W5Akh6WCCTNpNgBP7Sk\n2HUZu2am4ohrigmlUaapavQ/AENrmGalHeZXhNKjgSTTNsGUlztX4a2MbhfCkdXQxUdhJHGz\n5eKwPY4cAukhQEo0kqKVuwQkrVwcmhgY1FEZRiP5qFWX+ty51N6DIxPaB3qlPPBVDZ1asRzC\nhUTah3cYKSN2jSbtnIMdswasQlwyGJEUhNFfHQoXohCSUnB0VSDdASQXoiN0O0YkRSPJ5uMY\nJMnCKHbkeChly663twBp4TAEktpT2ttlUtfTuRDb+nAgxYC80oNIiutI1fOOSwdSDI50R5cQ\n+ZHETpaLvfY4ShVISQ4Fx0EW0hEiKVG52x3Yjk0MkrD6Z6Oml7bpR1aRvwNHM+wttS1mzLKK\njkh2iNn0yq71mihPVs5UgBZWLCrZVDk2GeJgFN1B3oekJByFBVJS/Ko/+nQPJO+rRAHpApL2\noxM5MVIHH/SZTqwhE0XKvZJ3C5A08aGeuQJSVzcnjKV3ABIOJZQXgkgsjPCg/IrpYITGwigW\nRz4kXcNRhEDyYyUFSDsk+Z97h3JnHTiOT+TEVKB99CrloZ91x7DWsbV0I45mdueqzKxMqSK5\nWso7AMn01oRVqqbo3R5EU7MTSffjiEcSe2cnax1xlKzZnQaSTyQ5Xy5eJIXNJNfQR05Mh4lq\nNVYngcDwRs4Tpt0c60ndB6TB7VKg7dpmdUsktxF/ByBh2jHmF+E3NLNplp4HAlQvw8hHCTiK\nEEhpmt0tQGLf7qpytx5wj378zAjwMQDz0oJJK6qLh2/CEUDmKJA62kgCZgPfeAftJ6vjpV56\nByBRKBSMpA4dmY+KbDSMUnGUotzN0YpdqmZ3I5AO6XoxQHLnG4WUO24ComeGKjiYniyYfsds\nw4dwdHb9lHrLBVx2lDWFnvnE27wDkOZC6JiRjMo41KeNoxPjGY+kOVaxcwmk0EbpjUA6sH0E\nklKVu5nnaBZJxxGeSInSaXi0p8QIgwCQkuedSO3GlsYqF7OrInaQXgykSbloamuzmz5sc9oD\nYTR7XXf7m7uZKoga13bOA4F0+HcEkFxIYpcOQpJvFmLnBhxNDfT3Rg2kMF2/neTDkX33Njql\nXLkWdCV7zYbDZwDJ6MAYD0QOklwlHqs02M2iFA+ju8wj97OjcRQhkBynnAZSukiaY0SS55vx\ndP88xM5Nr6x7MJGxjMPoqSzlAZJ1FsqVOGdBvl20NRfW6V0tng+kqdb+x8qKDTFb27ISouZ6\niD0dR9uuCm6OcqAgwtWQpOkFPX6JQIoRSf6vDiNpf9jDD8rDNCjFCuoBZaXbSR0jkEz+W5BG\nOjVX6T39aqKntdCbXwEk07WyhLSQRiMfy4D3x63l18LIeoGZL/kWQs0Jze4akK5bScH1I6pg\nMTc5w2bHcxqoW4R23g25J/ouPPcqeC9udqeWHoZZo7p5ZMu4Dn30Aomk5G67dSiohPT94L0c\nR+YdvNV1/Kj5DCAx20MekZSApM2ALpaJwzWnXbYIhDxnmTk0+cJhZPuoL7TTXQuiQNdIJz0V\nSBIFN2TMTyiQIBWl0q5P+v5t5GICjB6HI91VgeEm9YcHJBEBrPcCyaHbJSFpP7A3IGkznkXm\naBAxatMYINThpgjXRMI7+aggatUsLn0HamcLsq0Au312IqPiiUCC2B8Kr5UFdXQuVVEZqoNR\nHCoYvweMZuqq4MeR3dbljKvhsUBKE0nHsT2HpL8bvXgl2v7co0SXtYH/Ynv70pPW4Jn+gopP\n4orcx3odRujwJ5VCeEYgPRFI6I9DJ+eAiDeebr0bu+92lACjW3Hkrm3LsJL9Dw4kYdwkBuId\nCz3y+OaAxCLJOboXkbQZSbUBul/zMeJSJ4Ln1IaPlyfs9MPuPkQ961vG9zvR5Y7rMwLpiUCq\njJdTQb4kh0k/5M4RexGOqBvNsSo5w0jbf7uY3vlT0GQKACnwHg4gRYokbnwvIWkzkB2pXsc1\nn5qbgliAZsJiUv22OTAx0w8e7JFMJBU3F18TEmtsVy69M0zPA1KXmVZhAyFqaKliaulwsbwI\nRmtljN1YRuHoH1dfFxeQwr6HxI2jE7pdCpL4VNXQVB2ajWH6RO3Moesh4U8iCGh3KcfzXZXv\nkFwMgApit4k/jU8ymhw9MmLpGUCiNWVZJgrLsZCbQ7UjKORlOFpeRrRUYzzcMTiudkOEZnc0\neq4C6aRIegSSNoSFg9YsvqPaNZKwwpRWFUXKyofjIyjzptKhsGVJd4sNdaBE2lONMh8PpFEo\nLTWn8t0orpWjcTkEffiODcpfBCPM0Z11G6xVvYvHUdz+bKKvIQiksJF0USTdhiTdbAzFxKLU\nH8MYCjXwXQ6bSsAxtRBc2Y/9EyhtD9pxIY66zFRajdxgnfruXOPmRwOJ9q1RjsM6UawxGNmk\nK0Hv5FEKjG731um1srZ98Sk4gh+DsAn7GvwoeByQHowkWPEHHRJK7qe9rMCktCbvF54mI8Dp\nmjB02ItFM6tEHGlx1meX6ppE0aOBVK3jsHxOo4MwsAnZDHkfWbUbyZfiCIaedOrW0u44BuJ/\n9MImJnvWj4IIm8jB7lFI4maAHYa4yt9E1BOJYvtJ6mfHbVcVfYfQwdZ70L85Lj2InBgtlq1b\nVsRWKUDLch7vujtJD5dI64qCJZeFFYUB37gfxTtglFQhf0etWf16oy1z/OP90QOb60AKuu2e\nLZLikKTKM2D3S9C6sOP4kcWxD1auCqOQoRRX3of2VdTW/rIKKpNcXKhpEk0Pt5Ea8nZmVDl9\nklrRy11GXRKMeHF0BUn5BkmIc4Z93Dy1/p0CpACHPwhISSLpOpJUvROMDQOWKBvWsqfsmuF4\nfGqEaFk1rS9U90vEUUMa3cCF7d1Kj3c2QHsk2uPCQjGVskI65+fdgqNLSMLOI7RKVqTmccwT\n/tEpbaK84UEgpRtJCSLpQUiS5MnJqc1VVkudeyQZCyg/bAIptY8XMaqyOOBIF946FaiQTI8H\nEipxiKQeONTUFSucrSnugNE5IGlvTW88oAO+Kcc6Ub+6xE+MZncZSBdEkke5u4ikEeJ1VERL\nPsyqECgTw6o8E9uf9C6fz+RBH4ac9ZLd+VwV99ETzISSBQAAIABJREFU9pHQdQKWJXUebPwu\nlDtwdAZJPUwtEkbxQxOfCmeA45zIH89V6nopkE6YSQkeB8JCOekS+mst8B11DsBMZdZiWVaP\n0QQxnarFajcbGwL96A903T0BSBRMW1Jgw2j64zJ0C45OIKlYZ2fUsbSgEnB8E/trVMDQU4B0\nXSTdgaRO62b1OuC1W2QIl0qm2ip5lTVUFZVbS3m1VIBfarpePD0jskGsoXXU5+kYyWbRHTCa\n05FU2xqD7vs8XsZRTC1jh6/hOpAeJJK8SIqZNVhWwTg28qZehBPDDx3jI9Bh2j5SGy20z0Jb\nso1VD0JKeW7jlaOnhAhla98+2A8IeCNvwVEykihS2IxznZMmwDFN7K9RAUNhmATPOKvbPR9J\nI/YzHbK1YnFyMcY2AkhdjhqdyseW9B+h10q+aP9ZekrQarX27YvJAL4FR8kiiVRPbCi3/six\nTPSvJ4F0+OQ7gJQgkv45odzFIgkMFdOrT2bpMaI9XeP3hM9TJddYtFkJI/I/tKNz8+USPQVI\nKtYp9vXvgNGcjKSSvPT5UK/eVZZhYn89WaorAkh36XbH9szq1GQkhQt/r5NXrA67NIEk+6Yk\nr1zQEw4ktAO9o0WygpWc4o5u3lp6ThqF8tSNIu7tb8FRKpKWd9SFI0zUiptjbhZIzwNSkkhK\nVe7UQT+S1Px1WrMynrtDC4ojjV1lvEB9nCdc5etAd4k6U/3rVNjazQLpWflIiaL0FhwlIknC\n3Mo8s7Yd3BzD4eg8kI68fQJIZ3W7m5Q7Mz9RSOrJYSeU5w4Fk1+2DAZEquRPjCecUpxmhBAi\niX5FmRZdRDKOngSk1tHqyEO3wGhORBJuC/aWy8HJMAxiuI0lB9vG/HIHkJxCKgFISUiypigK\nSSjzGyVQVEKdb6lV7ussF7UuixrlCccHlSj2jJ9Q1eu6d1PpWRmy7jgGlu7BURqSKpgSmlKs\n/upkr0cIpBNAcjn2Yt7UrdtdRtJ2kqKQNBunAaEBerjyXgfKdi2PQiTGEw7LOHiLdYVxMJxc\nTSku0VsU0XdsGNyDoyQkLfMKeR2QBw0KvJO5OBxFC6SHAemskcR/kke5212wPxqHpDHT0oTE\nSufdLxXu/dQYTzhVrBqkihnqjTrYVaK5Sy69BZBcW9i3wGhOQRKkeaAG2qORxDDXRYEUYyKd\nBFKMbrdHEnsp/RyHpOPRKCRJ6hg8mb6uhRcT2PJlb9gYoRYgka2RDTqIVbn9bpJMbwEk1+fc\nhKMUJJXaYQez5WStGwTSU4EUEEnrj1eUOxdOopA0lmDlVIISHqSzCI5F004orZ7wCKJyqmCF\n6b1Yk3x7T87fuwDpOBw34SgBSWtSny8bNP7n85rdM4AUOJF+5oGgz2EORyEJScDcF6h11eRV\nqwrROveWME4PrJ2dJzyKZNtu+p7kGIE+lTfFhr8DkDAFyKfcXbx/NJIas945+cq3dDt/PeLo\n5UDSSIr9rhCS2MPRSEIgjUYytLgtzkiJsQTV++AJT6FeLdo6ZnZK73LppHcAEgUEHz/nJhyF\nyPIn6llxcpWP365odrcBKd5IcvzKfbEfSR6cROIIZr8nHoB52IY8HqntnJ7weNKdJkirm1xd\nVk/RK4GkxXeR5aZ5rE3PwVHp2L9wc9VDBFKM0+48kOKk1AmRREjyASUSRzj9AzK2pOkI7Ayx\nnvA4mqhNLdYqgl3gQT2uuFjY4YVA6nISQpg4e8wqnh3lbh/xFg77zMlTqQLp2UCK1+3c38B9\nMw+GAFQicUQV6AolKKZNaImbGE94JEH1ogErDVGVFZWRcDUf/XVAMmVQGuDkgVHuHv4aXTSO\nUgXSWRPpRUBiF4kzUPEe3k+BKsyF009/BpQtpyc8nmSLPjtQRLD5KsAWXBC1uODAey2QKGyo\nhE+qEhww99G4KJV7zc7JUH5ec/7swtGNQIrxNjxYJJ1DkmMaujIj1Qq4oB3yECPsPeEnSMcI\nUTSeyuQ8VfWb6LVAIibeNj58Kjk0cjc/8Ti6JpDeCEipIukkknzToYPvwmQ84WepgZYjQ9mi\nqY7VF0vxwUCyQndldn9se5BwZduamW60vBpIMW8TrdsliqTzSErE0Rqn0AlR+cUSecIvkHL+\nIRiVOtRbIF7eoElJlHopkLZ9QpvMHxL/EKr3SHJzUzKOYjW7s0CK3n+NfmPPB6ZBxXvYNxeT\n9jjJcDT4jJ7wS0TBDqAfyoN0U/VvEkTeS4HU5xthWrxCueu3USJuXvKw2WWB5ACSg/1iHuv8\nKVq3e4BIOiLJOxW1WldN8M55RSuOZDcgv4mDe0Nv+cZvMb0USLJDcaDruRwXhqe8R25NmZuV\nOI70cqULSHgoLKTuARJ3ZVqc0z8BkZSEJP9M1DQPk+rGXD/L+9QfVaEhz2rZp6SjvhZIaKPU\nho2bB9YdOxB4imqs448eB0+Vbx+TpQgkqzzCo4Hku/JOkZSGpO0EjHsmVTU9dFL4nD/JZnbW\nNAZ1f8zi0/9eDKR5W8EiYmvgSoF8m1RTEUSu0Ar5378sK90hkLbn3g4kFRG0Z3Tn5zBfyX3k\naTPJPr6dgAU2e9VtUQ4q9KAVWOk2EA1+F5kCEkdybNZz9GogVcluzHuQBGrxuhlR0xIIs81x\nkpvFTmh2+xs78HYaSE5Gjv8cz1d6gBKMquNw5DCCWuSLAhxPzfSU4vczJCYxkk9+DJDUnnba\nHvUdSMKwJNDG1VT28ApqwuMZLw1HTwCSg6cZ3e5OkRSHpO0EqE7JDneCRAWlR58udjcqH++A\ncvu5RytuTYZKJbwUSLUqqZS28NwBJKxvNrfgJSyN81tPfTyDXdHs2BMj0RANpBt0uxuQtB1/\n3XHcsfs6qpSjnAyU+qbo7FSaOlSWhnmSUrYiuDXzbCBNpjYmbcjm3ZAc7HEDkgQAaVn1xnEN\n/rbmPp6/mN+dAuklQLpLt/PGoHqB5PDVIUo4iAj01o1TCWyBTf6u7hglU18r/3dr2tCE3A5P\nBlK/AhvT+QQ2rk6NiT+PJC3Dl8nqltns0Z6knzZzH8V2AfdXhGZ3M5DijCTF2+4Xf4RIOkap\nYlvEglnkKQB80VIaUznr4TtKNslKQweyBnXv1kBJ12cDyfYsLIoVDWR6JshpJLXq6djBVJXC\nIIm0n/w47gr9HhBIzwGSfabzx/AX/eMaorNIIhzxiQtkOZf6zEK4dcAHkYaRSnefurYdirB+\n+UwgjWLhX7GuQtX5xhpnkSSycup1wVuYRyMiD5MfZq64/Ri/ZncBSMm6HQ8v+4WcnxROMIoH\nEuEI5A4pIv3RZ9aJAvmkJNNEZtXTIl4QxHmz0ZGGoIX0VCBB19jbtthOIqlAZ1CudIaiMVUJ\nXdPv560YgaQpyUR6DJCOrB3/TUGw+I7tkUQ4goUfeQH663GKmzHxH99L2bwdlTPe7ivFdKF9\nIpBkhKYZTSeBRHIbhkkFRqpeCO7p98LFA6Tok58EJDdrM990+D0GLN6DGyQpHKmGRSoRyI0k\ndxWUiHL7l0gV7rKe4tmxXemZql1zp9V4Ekm12YSdau3t8DDBKRy9GZDYdAb3a+5+jgSL96A6\nAUjhCBb5RqW6ckgCh91BgaF+6OfmPpbwGVbD2aiQu6c6G4rkzVcPnUMSRjMoh8dk2h8GGCQF\nME8DUrT/OwlIOyTtoRAYKB/RSFNDYWyqMEtUo3BGXNGp8hhDpApOpff4SyRcY7V7oY3i2ucA\naWhRNK7Vnu+gU0jqshJjUzbGq3f68T/ReEmLCHXv27rewnWx8yn8N8Tcc/eiKViJLGWHrh7w\nNLTUpmjIGRyB+XTES53lIsKHdpVUZ3SgKU4EPgNIKCqx/U17o5V0EklUXBPWNKEd8f7JV3/F\nAiZNIJk/HwukcyIp8kYxB9UJQACOKlONRget6h1jwRlq6fKHpwlI/T51cC8W6QlAUnHWCGv4\n+z6xnI4kGhFcC0uDae/kr3/H4IXh7zCQbD52vUb01aGvCNx0vS8DBS9QPCNpO+70Nms7rzhy\n6HEsyWeFs86mk3iQHgYk8/Bm4dl+EGTj36vcpSJJN8hRviKlH/gn3/5nEBn87x8FpPlcKWLv\nUXseKP5GTCuO0HaK1lWG7DkJFkAiUo98GJBMaLp6kZLyyNvsVrH8f1KgVK7aLiQ/5mRBJnFG\nCC+8ZncJCg8BkgdJPjR4x8tzyKJebdWs8mhcfov3xZXPK5MTsxeL9CggtfB8cIpJ+uhKG/ji\nZv02Hkmw8wdd5dFVNzV10GPnZAyb2aJxlGA4PQJI6SLJh4fEATPXWdSj9LH0usVkjXLnSim7\nhndP3E9THalBPQpIIIAaUHslbBLIggJpoYTYzU1wo5HU0tJS73YnvGzBBjzoQ+8HpBt0uysi\niQOyg2z7KMKhbXVy0b1gnxLwMMY95VFAUvZkj1YRiKMRdt/u89hZFIkkSGPW8QyrTDzBFbRN\na/1jw4MMZ7qBFMvE0UC6S7c7hyRuRJ3zscFRUK9r8xVFFOyMzV2eF8saoofZSOhjht2jIlN2\nZPWosnVRSMLMdp1bYsxHlif+em3n7aEDH0cCKR4JTwaSRyQFRsU5pu4JScKRWpdzIUTdkogo\nn7I1G02PAhLVsYSv7LQuVdzp+Y4nJZnBZYqY7uyiGiy/hII0t/8+cmAEz18H0nUj6UaRNDNj\nys1Ln4AjpUpsjOspL9riuZlKPnoUkKbcFLcocQMbHOBV6KoHUK3FIOkGUHarNPYjxy0+hvnr\nxtiRjQMs/wZAOiuSAuob9/uOui2Ourb17XuO5R5wsCqLiHDS59DDVLth0mEMky8y8dEEnlYa\n6lGvaTJmJzZJIBE5+O/5QErV7dJFkntkNoPO/X4kgyNiEq/qLxws1N4ZKXOJHgCkqauo/LgO\nY6AmHM8s/rjSMlO1TjDvummWUD9TTwfLKyezb1wHWMS8NZASRdJ+1JkDB81+lUeV5Y3jyFSO\nJAKdvfxiILXGOlJhDBKCVtsT7T5voalapqvQwz/aBirLKn/PCCT+wPx6IJ3Q7RIWksOguw/I\nQ/243mAHxBEEQXpl0iaxos/K9tZ0gmt0F5AavcqXyu0NBJiq2tetGdK8Uq40aZHp6sRXcHQP\nkLhfnXe4BsR7RdJ+BBwj7zrQOySOiWeg/KQyEB865uBvrU2z2TdygN8DpKnQInb5vEoOet+Z\ndLro+sk3k1Sj3Bj1e6qFqRnB80m6p8HLrNyy/+5AilTu3GN/PFJbK6w1R8v/+ooSlCYIyPGv\nuhN1BUMroQJv+JsodrdJJB39pMIH5r6p4L8xrXUfRqNpGtO7li6WTf7eLJCSgOS++2WR5os5\nYJHEDoF9N27090fUmuoKNq2gH2wOTt0+xrPbq2mVr6kcydBNQBpzWhoEjNRQG6E7FucbuV+l\ncrWHHIWmeS45i6PHmUgXgeT7Yu7lYkXSftCZnULq+MH45YRqM76YO02GvSjLrOTb5fVqUexf\ntkS76DqQJiVdx7KhwFATzPFaD7/qr0uNfae1dQsRyyN//YrdnQLpGUCaA588u1+Df5XNKOzH\nfCzdEZ64nVoLJmi7Ro8Bba4O01zmpXevZFSZOMXbeOyALgOpywkwfWbH4GQif/lnFpi8p18D\nkzINtHkW+RsUSM83kU4CaTce7AexQAohaT/iXI4MqtZtx2X2SFTopPL1tsE9R9jal/29OaKX\n6SKQcIsIhwfMoULpU2WPkUEvBhLMTLcGoGxqGPEc4sfRKYH0CiC5BiTp9dh3MZftaSoZPQT3\nf/qR11Eq5BWt+Dn7vdikK/C/JFKGo4tAKtcP6uiPoe3RSVe93sMPRXHlqib0624ezyAR8c3s\nIe53jk3vBJKfxz1fDQdYIKUgSZfWOkgdQaZqqaXVWB8MpeVYkSugWaYtR2P5dji6CqTOsoaE\n/mMcZ1NI86UEvng9j7OVWcKzx9/Tit09JlIikNiXdBJ7KgMkr0jaPYaiV4qjMBnVSlYry0Ya\nNrGJfHpiVlkDwXoEfS2aV6/TW7pqI9HoUTNy1ZdpyF0D+lQS5M4BdbNXmoV11MMdYcWOF0g3\n+OxSgcR5ut3En8cCKVYkYVGtrCmP0y5Itx7IoukJMUeBs5g8BZgCBLv6bbIjoukqkCrsc0PS\nZ0Bxq1JHXhJZp0io6l9YbnxUtq45yvOGDxChg/eYSA8F0syewwApWiRRWaax1EuqTSUMPRay\nU9WAUXQxb6gV8Rdt4V+gs0CShdnrrBptJsJ49ri4hBoFPpRoGyuHqHwqko/eV6MJeHjjfoH0\njkA6HuCBFIMkEkctNtc7SpIRlAEQVR3CqKg9CUjixZrMBToJJHSB4aAtwzKVtnL38sWk18se\nlG/OqQpYue7deTgjgCO/QErV7F4EJPcRHkn+4QDCdauQ+F9mh3TdFel36sGGOoOjCeBWvkmq\nURSdAtJopfnW0PtOK3cyewMfA8ofQXH5datM20OjWA9v+A7yx9hrGA5N4dwkILmwEjI5eCAF\nkVQqRb7Xesk8dbv+ybogpJCWwep4JaF1vp5vMfuudE4i6eKpPRpG5VqKuHmtcUREjqKx1sug\n7ZD1sIXFguxRz4XsgVcDSbYiD9VT5IAUIZIkVQg0sd19ddDPGg0j24V6yKkgkxYgiFIrF7yE\ne0M6ByTMNFLt1HJwx5ht5vcQx2q+dOmZdco8XLHlQOao50r2wC1AYm4e+oyhJTdZcE+PB1IQ\nSQ3s54zU+2TQq9dWeRMQi7C6wmeuNH2JImlUMm655zuFpfrppI2EwSCURY9CaMrfy0rUGgRB\nabXbPEzxd7eUu456LmSPcAyawrdngDT1jVYceKvE0DmRZEYMI+1lY9XM2mpuDcyAXN0MEqSP\nI4C1ypTYQm7qXh2vmUBnvXbwsag8CYm6UxcxWc8kY9O2eazD7mBbHI96rmQPJAKJuU3az7Os\nrWqKRYTl6gFSjOMOHqFQVDWmABszJYCXca6dGJlIWDVYD6B/L57y0lkgkQgCJEG09xBfbPxZ\ntC5/fZzDzmWj7w77rmSPvARIxnMp2oG66jDD1AslrS+JJCP6yg5r3ljhwYZWNwN6eXn3B9bm\nxe4U4oOMpNMbsiSCeuNdmXLxcsf3hiyFXJGPIf46gWRd47sBf2i+xfudCKS/ZGbkVTfazamO\nhH4BpWCdFEmW5y7Lm1E5dB1RcNJyMxy8EfszBRlIGeYByo+Icjgf2SDQhFVImp/ZeTqWVhcR\nko8d/jI4Wi/z3YA/liiQbgPSXPf44ft2qBb1qnCPYmkWSDEiCetSgxDC2EsVj7Xj/9qXrrwh\ncoLr8mlYYf/96TyQVN7E+Mps8gBtC+b72OEvD6R1H9N3JXvoRUBCopCcygEjjSKgsG4XRlJH\nOhu6CUgzOeb4kehbC2iw1ADQxnHhLMVfT+sqdoEuxNoNasXgMojfgKzMifM4mlUOm+9S/sgj\ngeR32x373OtRMSiC9MugbhcQSevIYSJFvha2de0obvMrGaKktoWzRlX5rn0/dWdPV4JWq4wc\nYmX5trJXRjrs/Djy38FzzBNXnbT4nwASwehYMGM0KKp6DCAwp1wSSbP2WiMrTD0oeUc7yMr4\n71rP4ou3AhyaHL7Xb/MH6FL094dIXSAfJ9jccOIOnmMvBJLiv1aTLtA5aRSpmO3VDXEWSDR2\nU77ebRFN4+yqlCWMTVb4JVNfYd00xFGBm8rv5ck60iUgDW4HzRuSlxH+2jiqOR+X71L+GMua\njwQSvJPIdqTt+5JQRIrYRpdwv+vfUCivGjxh7rbC5UCleg0VABHQ2ExFY5FVb6v0KLqWj1R/\ngMxF8vHBXxtHbBKz91r2EL/GPxpI7R5IWncYLL/AdsVgYOR5jjUEi35CrKBy/NzzMHZmmDuZ\nB1IF1lbN47vLo+s1Gz4jhMPLBn8tIGkD4rhU+q7lD7FAYgHG3Ih7AP89cqWuynfKUbvaMytx\nMAqKJFsxxhH0tjeFBCUBIMYGB57zntkr9jI9rK3LW5GXCyw+kNgyGnqF9EJUMuYOvoNPAFIw\nbFW2lTLYV54cy6M4AmJg5HnQ/nkkjvzqfr08uUMJWXtNn/6TcPQzgOTjgb87A4maheQbs8J3\nB9/BVM0uGUiB+G8T+41SwnjJcCsn72Tftu1i1K991zgYRYskujPmf5UlJ20KCClDkAi/ldS7\ncDR2/VvqeT8BSF4W+GsDSdASadgvIh3Qc/A2E+kUkAYr9rusLe6b9tbTGqrNwMj/mfqBysCs\nzF+F20GAnUexqFCfB5pPHH4h0+4d8/1+gWTrQYJK80GUi+xVqJf/Dr6DZ4CU+A0+IGkQNXs7\ntjwASbsGOBhFiiSp92PVs92uuxqGFdA8zCNAQi6qprfrpUW978YvpR8AJC8D/N0ACeYJK08j\n7+UmHcZ7MX+MwZHPREr8CJ+3IYPYb1t10gs8BMQViw24aHayrwrLozczMAp86F89iA2EBw3o\nyyhhMJ2+O4kipc6MJ7x2W2xOapc3f7PcN6LvB5Jv+lcWUKSVOvyH7lHjx9F9mt3NQOq2mlF1\nzDefOi2cLN2O/1J+GP7agUJQSArzNirGl9BAvqxuBKRiF6LzjmBGijf0Qvx4IO3ObotFF1I2\nsP7vszS7m4FkSLZiYdRyZ9mvKMrXHDv/QHmOrk8ss3wkxU1y+KhtCxTWrjwNF/0blNjZ09cD\nyTf5B5YjUuU8TMmAk9z1HCAF/N9Do7eYG5utnSjyf2toLPUdMlXDowJLieF39O7hsak8YfP8\nAun55J17m+PmThRC8RRWXIaV8iGa3TOARFuy9epWyOfRUvUYFPnHKzCa+g4FLD9gbLJG0kJj\n21KZeAwYT8VF8YaRad8OpOgtebuJtHZ41aEb+I6mC6S7gdRpFJXNbu+l3KGoFxHlYSLHknZZ\nsep6HwqmU0l+IiZ1D7e9Wim75i0jHr4fSHGavY4Non8NheUU9l/tOfhqIGFZK3Hwfs8AMQtF\nE+AqpoBmYCzVYE4oLgTlqkHN1FkWjCuhVZbSFHR+S+0F2ixx70Q/AEiB+B4kMIswPUf2ooCk\nad3m6YJAejmQZsElxE2rLKoWA6WzHOAnP9c8k5xynVbXhhzbJuYuHa+LdjPILYzesbbQjwCS\nhw0VNbhlNKpmmZsF7yRnnTCRViAR/y+GRKgAUsy3zWzFYpUqO81ZuWbZnfxe65nKAqNb5srX\n4aBcl9SQ8B6ujPj1TVFDrSuxUPN2ah3QzwBS0EksaMvDLHmxfHUOSB6IATWkVqKTWK/Yyaxt\nfXznTPteE863jOn/YM/R9aFixQ4V53Inw5ZrnWs8y2co9U7f3ti/TRbPDwGSkwOso9TFdIIO\nCLLNI8uF+45dABIt6E2/YfPkV6B7NYV2nWw5bqx1DnfZ7SUB/03RLnCpnXJHAW/RQAGuuGCU\nzt5KFu2RJLv6jOf8YfRjgOTgAOsgumthUwPmEoz00X9pkK3mEybSP+ZVcIEm7vLvZXmPIKtK\nLN1Z7IIMVOBqcUCR75viRZKiVn8IY6rVWKBdrRZ9wIWwIkm2lVVH9k3spZ8DpAMLbA6qBkqk\ng4jD1Cdz1XkTaXl4KyGvB4KnYb+y516AnuPjaWyADGkL077QE5Srb9y6VAAovsObQVV7raN0\nlGBR1I8K0iV+uL9Ur0aS2QQr38l/94OAtOOB3TF7YRP7GvD7a8NMdR5IxC29AtCgllz+OT6W\nxntlsCxUuz3PXqNoXD5WbL/WjxPP0b/2qI6mGoo3ixo8hgvkyGPuO5GQ1FK3ZtEO6LV/Gxz9\nLCBtuIA/aXAXR0phqvkqkKQSiuNFILUKSK3rk4DDqV2H90NjP9o8FgmjysMbrS2sGFOeNWO4\neHyvwsYJPm+Fo58GpPk44TtqhDg4svbXhllqPg8kpcxNAwKpvQlIruW+XuwmWOWLHUMGcOI7\nbI3r6vSW0hPg0OmNhzwCFpJeG898Lxz9OCBpPmAP517FO5alvFXiPBCbaZtSq2Gy1n6PdMaG\nqwTcCYDUO9xbmBeEmUP7fIcAUAKHFWnndlt4R3Sir8VMMHiJiGo6iKQ3w9EPBNK8m/At9UdX\nsePqEEfNZwSS3kYq1/1LYZb1c0CCbl4dOiTzo5BtVdNSAcy8OXoaSPZNJErCSbsGWDc1vEHb\n6C3x8midHkmlYbwTjr4cSL3az9iR10Bqg8U1ohjqPJAWntIettow4Am+hstot2gxj5oj1wmQ\nQwikudyFHviB5H3iSv0ikaj+aimYSuD6PQBEdU9NrWK6bNVvh6OvBlJHhvQDNhoeCyTrC0S/\nPvEMkAbPVksDkqpHmOU7n54HSIHwPpsmK8ah9Ljkmkx5dygYPKJg/ls5vom+GEim1miw+8E5\nOoWjNCCFH+Y/BDXzcwXGaa/b9SCRQAFTYinq03iR5HprrGmNOtvgy3yY+g7Qoxs5B5MqlLr4\nVjF33wukCXUK2gIPpIGVvjiTglsiPwJIhsA7NmxE0wSiCoG0t5FOiCT3WyNCW9DWhnAIAkov\nAb6JQJofCK66/pVITyKVWt35TV2g2jd1ku248UAgRUd/RwOJWrtuv7NSnekdFlQASPvj3OC1\nKI3AAhMhAYJheZDhP+fBFie4n/RW8uibgaR3TvogkryTDOukM3jZz2oeIPkwhpQQ/R0FpLFb\ne5zbiBkzVlz7gHR8Jjt4nZWd4nciyLWxbBfWxN+w79hXA4nGm5KMPOpC7Tk4HTf+FZ3FURBI\nKdHfEUAycd7ZoVgk1sAki2TalWgMACkuRERvEnW8x0OTeDvvQSJ9L5BqLIg/A5Cwei+7iuW1\nR17VuPHvAJqf0eA/J4GUFP0d2s2iSB3ygTuGYOpqkraLCdM0dka4D0ibh/qmAIVrITqACaJE\n8l36yndtnxpJ3wsk7EffqQq5Jb+P0a7LvoOg2qpIVO6sQylAUncVzuhvD3mBhEnaedVK+Itn\nVblX/EJAmq0/fURLwUSp5o0jGNi8wWc0CGLpe4GkEy9RnvAl1mavX68FV8PIIy3AZkcwzUEg\nuaO/PRQCko4cLZm0iXlstNiyKiiEkBQFo5mwBnFEAAAfrUlEQVSqyrSwpTTLwq9hH2iooCRr\nwgWvpC8Gkt5IAr6cPFOo9m2dyzUVx+18miHLZU4whU0kd/S3j3xAWkSqP4d0WlG0KXIXApI7\nPGQSh7CQqWt1+C0gNaEnS/5+u0U8fSeQxlY0khKqSfFvfXqNQlxxVC56ZaLU/gkNA8mAKcrX\n4Ij+9pEXSK0fSMKyyKJuat36SDXrMRhNrcr4QBNVXvAzdL6vBFKzXcpaNNp9S+FEm+pir/uY\ngm9liAP2qzXPgCEguaO/457t4nb47qkVeSYcS4kqgFINx4r36UBqUYQ4RbfaxWqrmO9Zr5Iw\nGjHBd6+nbwSSjg3SjIHzG/AJYQXCw5R1hb4JetD9aslNQHJHf0c8mGd3bS0e8+zwAbji9FVC\ncIMLSX1hLNI9UXswbCxbpRZZqD9EuftCIMHWTw11bdWM6XqeTupFlhMLDYVrxsz6KmLUEsVi\nXv4LAskZ/R16qo/b1wrgh5AB4dGckoAk14ccb1isY9dViZ0rY3Tbd6AvBFKD4meEnZHFRILg\n/NZVLAdJqTa0KekXWk28gu/jvzCOLFqjv089Tt2ETCA5OFzpneeTvEDaBSDRONbCPUQyKunc\nTeUvkF5FQndUJvLtlxudJ+a+Qx6rrt8GpIuPo8MC9tAwYa446okFZpn0tRDNYbGJBhINo5Cs\nT6ZJqeO4GHRAddtJifriR3jAvxNIMJtG1fCE72Pwg4RTqiPexmqfzNReFUgvARKqhzVIaXE0\nYAA9rQoi2nO7F0grkujyRfRX3sGOpT7b0du1QnLSFwIJN1FVu7jO22+0QYdESxnX+wVZ9WQ8\nGbjCMt95IMkB+prQcr3xjwV4PYeB6EEYlU6mXK2b3ToRByQ0gGDBKW/BkdVTnijsbHkL+kIg\nzTmaPD0CCPYi2BMFHssXjnSYD8oJlc+yEuf2113Mx+IoDKQ821EkkAQsJdPyIe5dHuJb0dbH\nkYpCUq1kWRqOIO2oZixX7IYt+3bRN0V71rZ6Nn0jkKQ9+D4gVTD1tQoh2i3HPRYJhRg9ztKq\niwhxtec99ccZIIk9kPTTPeICqMN9IloXjmwJ8U8VZsl1B/EbBaQ5x22BFUdj59CT90TRJG6U\njEc1onn7bdlvBNKGfECC3b4Olbr+YNLWyHUTp/aQ5kcZCMF3OALJAafgXURJNjgQxvXoV2Ih\npAgCYMk/6ZAYLShPI7pRjstFFJLw+xWOFhDxu7I2kSXElM2Xu5WrzSPyz19M3w4kPmgFSBex\nm4qD406YRi+NxGaLx+PwG3gk7O5CLLmAFAAAQ1NXubHNEWAeFg1nCe4Wub6AFf9o18eJJLWq\ntP2a+hTe/Wok7IEzBlBvf53a6Y3yrL6OvhlIU1bCzB5XvRFW5nHU0VzYd3m/WFeEMJrQKd+f\nAKVBhxFc4nSL2PUyDkg5u380Nto3sI9E8NDUlPPIWBski5vlY2sHNOOQNG7Mt5KRfUfa98hY\naXWkU9PL0us0egf6ZiDRwu0KV4DFuQCgaCAcxVZPCooscKoP++sd3dhwUOx6GQUkpvj4POgl\nP09A0ZaOwdkoFkbs/HJk6zggrdZb2fTkDY/yWQsecMKOcsx7XPLeOoP2i4GExQkdId2kg+uC\n18Sbjg3D2v7xED1OGhH19BnqLNZNHqMn4W0daptJna1PGt5Yu+FwYwENXuwonrj3tV9aqvfq\nJ6WHMS36sANZvyqYhWfYtEzG/MyB4jPeOYf2i4EE1dvd6ozKB1Xrr+zcprHNVuV+te5wIdc9\nHrrYbcM4IB0KNtL74EuLcyiSLXkBjjce0RfRMDZXnEiqsqqDoVbNxznjrVMCtahaOc1YAMCr\nEncAJrRQ+9i4w5fRtwGpj9q/65NqsFbNsWwDuPPE1Ou5vRlIy/sV3b6HQ6vVOq5XOUvDqns5\nzPtx+bpFt6NdnTGiwxrz0ibiDm/kSPFzbIX5R00VLB8pNqv8Ve2eRxGZ2UiqEhWEtM4hTYnK\nHuyXTtxeqpSj15eAu6E4npQ2rxnO7ov1x7JmxKiTuvXCwjU8vSohBDnn+1U/Hkk64g7/0TpN\nms2HZSERo9MqQRi9+zbStwFJRJr9UAJUzXswmRtLUx9VEO09ayQqKVv9vcoPSYJIV4A077un\nRkeCwrJRLNCbZMVfNVH/lb3DwQ+k9bU7HXEH1KteFAdqUJuTLcY4OUFtUatf1uVPfDf6OiBF\nus9krRX6LLTc9VbBU5uazKJdXHhO2v/U7a6NBJKtAh00JIMmVgiOsm02j20GfRcm4XTq9Lqw\nXwAigUQVGYjde8jsEm7eL3ZiSLYDu6GtfS5jcIregL4KSJOok5au4dpW31Sv7L6daNXGkaBm\nubBilvaVJFDr+K1rG1wEHFcsRwqfuHJdZRVUWNh8z7KRSCr0I3F9KuaRD1own6Qz/N2vCgoz\nTE1sr5fX0lcBSSSjojsbfNJTrmBPvrD9Mq59eoqMtZAGpB3tDYwNJGRbi40pz3CnHBw+ZBUI\nJVoJ47FDUiSQhop8DAobrJhpM+OqMyKdgwmUl8TWfO+v2X0VkOSWa3my6wD0bVrqM1JrPWhy\ndEitwGYC9ixEacureCANbbWgc3NnL5Cq/bGkBaU0gRJ9apzQ9sXZekybZ+G7jSQ5sQkZJ5Ni\nkjPfhL4JSFSCMLzbUDr1mx354vfVUtrOUyOcTV+wPDDt1qJTWPucY/mRPkTOGw4yQMoxJand\npBlq6bf83lEUUfALLRrMuiKvACmqs9tITSdoexld5QW/nYQjXXxC9ZOvApIa+EBjMTinCoWz\n9h6NT4eEFxSqd9yZgRN64G36Z2G4OhZHdH8pt6+4WEe8YS4qFTPQBTwRfpqOm9gBIK2v7jXO\nVmrFZKK/MdHLW9+E21R/N/oyICmHl29XdkTuHAMRYcJzsME71FhSxPk05JJCaPyIVCAV4Bpe\ngGTZE5EkyUTJuZy5UxQLJHg2WUpNUfi1sdGooMOJEl3vSN8DpFHZKmgtFPx2ZQ22rY5XZk8T\nHkeRAgY8psVQvf1dpnpz+yLUUXkHJII6PCSxFpUSRsXNMWnRIilXu7FlUMMWhLmpwAr7H+Dd\nDtK3AIlMV1yIMaKb5yVUPuDsxrcStn5pRalKeIq70QXtbjZQeng9IRJIHcohlQZvvaGUmNQH\nGdiiX/s/6RFQ6K1uZ8poIKn3UaWGfcplpoYeO5ENH1LfxEtfAiRjiKNd6qiWs9LC1rhv0sGf\nHJAKjwEMMq/E8qG6X4TzaT1l28DaqxNBI4HUMkCy3dvtHkhKpzsW1XK9mttFwlI0ksBNgu9f\ngv7L+wikdnlTXb1PcCaE6EuAtPAYhpYqD4FTHjW6gR/5ixACzOrdK2Z1k1SAJdOfj7OTIIhE\ntnJUJJAGNLsASNsyCsICkoB/2Z9J/u+ybR3e+D21no9zUQKQoFDRgDA5Jh2vZHyDxTdodUjf\nASQqrjrB//ec10i7wNZiJnw8kYry5By5mxZ//kYXCx4qI9oigYRFFqCXNEgZS8II2HVZqGoh\n8Gjc+vBaC2XLaXV7fKlav/WgrHyedu7BeCSBYTjha7c+RwmFUI1RGxGfQd8BpBLnAzS6np0Z\nzOSD1brBqB0pK5/yoYomMqrSoFd9uWd2L8UCyaqRGF3VbRBltiVHaw39k2z8Jky+21SNBtKI\nb4wJe42vBEoLQ1v5xP6n0XcAKSM9aLFFcnb6Fmgsh7EnHTWhD+y16KwFrx+3Vi6FLTW5U5jF\nren47FWDSyIIw6uFUgEd6UzrmwqvKNiXHAoBaX193Y+9Atx6lMwPilmIo+8AEqQxTJgG5lsH\nB2Qm8owDq4ZCICg83LvBWLp4gZikw5hYcXybCCBByAQkku4l5kQdWNieAPTaPWXD7qCsqtGP\nhBCvvCDV1q6VFQ2kCtRlZSR5o5QQq2xK+ufRdwAJU4ZQDaq9ihat1bCz3ix2OXta37Ydlazz\n3265ocP/pTNZlRc4rvus7yHrewH2cQvMddNpEUdaGjmUJh1+V4BHBm6y+zDz0XivaZu5FC2S\n0BKl8raBkATdEvNL6LOBNOm51lUJHTGXdKKuz1CGFQpVWUjJq3Sn0qLX9dJKVvLykwtIa6rh\n2Nhx5RPyZ+HS22a7hLdbLdRfpM/bywvEf9lb51tnRAMJJE2xlgGEnntf4pYL0GcDSUJBuaoY\n1T5SWXDFO2tjswTLNUjDh+eCvEZTCruSEp6WbAZIAyS5kSsQlDTCai9cTog1/jsXdTsckCYI\n0r2ObDqEYyh89ev51tF4JJUWigucEney8JfRpwNp4dPMKDysJgVMkusjqg0JN7vg3YMot2Eu\nTm24K+ZvaMtxTExo6CnzosAYhn4be4E7M+hvaxwBTNCEQzT8NhJYhXVbQjHLwhqNlYyookPZ\nSSBRyB39WTll41fS5wNJK0+IDybGjiwHfRCTz7hQOiqfsvDz3J7bLJxoW7hXMBSJOyVWTYa9\npYP/EPD/Lif/FFr3y/WO+1pBSFAJncKMilZC2J69AgSBtAlvUFoBRVs4KyV/HX02kFSpAOX7\nYSPuTQCRVjJGwW4hUeXHMuvYkhtjID9GqLWcuClPrMa2T9+zNFV8n0ylOoXuM3aHShO20uUg\n2ke1jLvN16cAiUhtxX1If6Or9OFA0lUT/Vui4D7It24GVtagHJKqO4MTSFXA7Fm0THN88pTt\nYahrK4pggBfeVFAolluNiNKKB9KIZRvEWoLHor4SvlrHVAvJVKLYfmSSSJr1Nlwe3S700+nT\ngVSQ6QxCaeIdCNhxZzC95XwEfZEmLFc8MUByGOpbmszmUbGTKXHUMg+oshyqIxdM0dHRFD4B\nxZXzXwLJfjHAjo/o1G2nTmSHMv5pQJpIma6nDtNhfwB9OpBm0VFedrVwf8HyLCJp6u3aaxzl\nWqPi9pBEvLIGdzoT2uzmPRXv0FDywYE2ZRsWoSqcchNQkm1MxvWQF/VJQFpLwnxLCFCIPh5I\nQBHNyUcqF0R6u9f8JXYVjjLF6k5DvLZyydIe+73JR9EA06LfudhTh62C95tDr11BzBmNt4Ya\nyJ3/L0Wxwy4FP2P/SNNnA6lvabaGcONkhSTiJK8BbALd3FtSVQiJFnlP6xwB2kQtqEMgOPai\nb1kH8NHOF+vd+0fbU/SSI4RjoejW0kg4TFsfebx9pCw5uM/Q/hA8fTKQgC20QV0frGNN2her\nkIQRdC5GnIAFOwxHI+WH2WkiTixPJqOVK7cLztZCf4fyojC3GfkyKPAlzEGqlCBakxC+P66D\n+EzbKFvyxaIISOCqpgYy0dvymfTBQBo2lvzIqDOwIwqNe1Ykza4aiaCOLPZ1rs3tlltJ16qk\np5p9GbVKVGy/uhwYuHBrX0ScH2SAtaRhRW698bVwPg3VWQqDEjZvGA0jHOvWUSDzi+mDgZRn\ned2Bg9nriFOm9QImquvAc3+GwTPheOS1TvYJR0K2I0ePSwlrOKwS47G+/yjR44ZRDMfA8kEV\nTOF4N7drjDniVuFXiOVtFOCarTSJhhF8hFSWa1Uf2oZ+JX0ukKjteJ+zbINk72+WtZf51eoZ\n463WWEoPIsNyYXb0wlHwdQCHBg2kcgukcRv2cPCtIMtCgBEj7Cyja2xyp3MGyvrosLxD26dY\nFNGXakBPqXvSH0kfC6QJV0vtGGC5H1kPXXCGOCSZTD40lkJdkwBLZ6rvjxjGOlJqbebSrloV\n443xQBsg7QO89wX2sYYpgHNyS1ZBj5Nd5Y5bpUSkwRSP2ZejSIARddgbVKfnb8kn99DHAgnn\niBJUS4+YURGqclLl7p0yQJOGW9n7SncYv/DEmBjCqx6OKiAcataN+2YTMx1At8B4iK3Q6iBk\nHWFY6+FCiYIAIOcS0lYKe5Y5d0oFPrFWDv5qb2zFw0ihFqua+ZMIv4Q+D0jCVHsHDQa0fukD\nh26PALU2WpH5QpEXHaRa91kYF3mtNRaWfDEFSCWqWC0H/+VzKqw0D/FFmw9blTGuyeysgMQU\nJLNEmtuwJJk1YgpIdzQpo1E062jdPBtg5Yi54LPp44DUKhbX6TMjrng+w0Y5Ycn4GRrvZqw0\nsZbMIqpYsZ471j4Kl0dFJAl2nS6UBlruY9TXxpTChfNeVfAq4Uw3lhuVtNgwKw/VmVv3m5yN\nMNnv2lCN8Okwze8HBK5+HJBAl8MNSdRTgF1G5/Jsk4yKsjOExpJb5oA/S2BZj5xV/fqwJrOI\nmpz1flNuVYlr+pbfjwHd2+tU6X6secfJTOmtSg/LU923TenG0RwNIxKsM60KpzpQfRh9HJCG\nvFx0MGATWVf9PA1dzoQg2NTFRNnJRgOS25Qiv9e4aFgNL3YWCRjcOCEFkTs6EWIaV1uNoWU6\ncc7ocqtxk/q8VWKXx4teedyEAU1YCOUH4OjzgLQwKrZcVozi92hbRBxSeua0DyezUm3WhliV\nE4MoKUOvVLvW+x05lgetd7qDBTqtlI4R22EMmXyk66X4qTC0UElgXx69+oFAUmnlyEtTLI4C\nebGzSlt3eKpsomCAHBxqJQckVTwoE/4os17k6RnYqyvELfNqre5e8JJJKNJf8SZgAjWwg0TL\nQZt/+a7sZwFJkkShbBf4uxcivhWQ5PNiZ9SLGhnoSNIDygYUXCyQjKHuvtHgZdGFh+cJyzVg\n1+Xj41FWcAW5lLr7foRW59me159BnwWkQlsmZPMkawseGUFaGzn4WLyBABQChOHgCSaa+rpg\ngIQoq6mfuivuNcNMIos2WMLc3JZqNgT0wumNGt1JXTHzm+mjgIT6e0k9IMqQyZNIdUYNSbKq\n8Sh3Kn4souL30DgaJym1rx04/cwPJFTbWtoyDWzNwM3v7Np3niaTcMgWbvoG+iQgKQ5TtU42\ndU+uk6rAD6zqs5JWKyVCHB50MyWoch1CcXj7tm0WDVQIox5ujuJDW4Bn6wXSKP1JGM8kWnry\npi2/W7n7JCDBnFAOM661ul3YPVQuk51nquiJ565d7rP2gaa+bQenNJCokYFvLe8xcCCQaru3\nknDDDPvbFdyl/Vq3IXuHTCCrHCff3+Ab6JOABBZMTunWZOw0N665quPdiIzuSC+A/6tKQNjQ\nuCp+G9L9lxxn9GR9VSq42tcV0ElgI00yKwfBAXlfFe/lnFuvsluU1Xsomw+hjwISxtaQ6UrK\n1x18ok1+XDv7GaoMO+Aps6qfYmJdlO7Xz46+3qoBn46G5jPrGALdVpKC696DXrdTF0Q1viza\np9G9Ddbflz4LSJC6WSrr6KZaAGOWK45vRD2SO8AlJzQ+AiTRHlhkWu84WaqQ656eWB+AhE/P\noSufwJrFw+5Nag0kJlgA+ndVLUQM8m09n0w/A0afBiTci61U7Nw9BkANNvlaMqTm0KKUJtqm\nkSW3zmK64bRYMK3LQ13YKJV7X8Kuf6XLXdAXaGhxjo61yrI8XVXil07RpwBpUAwykJ2Phsgt\n7tQKxEJh2K7gKgT1dq0GXinDfdoeVEDhcKwt64D2XDkjY4NACtD4Dv6Fn0kfAqRFpysaxE1P\nrjqZB31esbemAglZEfJnb7mckUiYVl3DGzo91FOlGR2bB7oeUTSo1UGgjihTnSktlnl5p93Y\nn0IfAiRBFnQ7rcpXdZ9LyiSZexX6RQqOpu4JF1ggKJFoDBZkbV3iptUyj3mRFWH8PpYkv+bb\nVu6RjbcA+afSZwBpFQZlh+H5sOLeser2akq14uZo+WoR1mYkLPm6EEHBOAiR9WpZ0hVzt9YT\nLlvX59nebQZIZsf4XZMXqhMq6wfQRwCJsmErxUbiRi4pTNKNrP2ixuqJOnVOLidas7nDWVJO\n6nVITXFMYxXZSm5etPTPN40jmALD/KH0EUBSlT9bWZvQmXvso97W6CbMw+bEyFiga2AKosOE\nlp3XXwb9nfu+eqjWkWbHvCcqwIsFN/gqwryWqqb5wlJ3nwEkxZ7luK7Xt+SJ6RVea3RdwTkR\nplz3y/P5CgfRTDPBvbpmwQ3C+5U9gyTK7wY3x/TOBU5TG4J+AH0KkFSaD7RBwuo2t6gGVpy1\nzkLn2L/GLeDSo7I1i9FjKrhd24aUrY5aZZeLzB21qvsNSk/C1DuQOFNc863pY4CksvkwXnWs\nb7KR4Ja6eqS/QgGu9CoNm62Lkq3bRMLRyCuGZFcXR3i73scJpD4jh+Hgj2F/OlFmX20+p3yD\nMMB76XOApAKv70ucAGpQYdQ+O9793cPGEVRtFO5QvHlNMadSV6wR410BrF5hReWFYiecMpl2\nZNusncq3yqQb9WeV6KwMp3N9HH0SkFQniFsXWtAYF5NeKhbm4tMaEDVUZ46pvbjxTRfsPo/0\n9ruNwpCXIPN27G70x9xEVoMz6ub7xnrnKfooIM33xqsiYWF66Bhbu6vKE4nlUEs2GgukXTs8\n90vW3oUAOaxpO1fBhjgCuajiWt9Ld4Jw48baGxjv2Qh8G/owIN2czYcktO+i5U2ShlIKBXrD\n2McD3AbFLYw3pPIaB9me0sEA7gYsLfFmbNqiiNQ7ztW0mE1f5bj7NCDdmM1nmLTJgju8k1aW\nwPbgdTNVVl+2Fbcd5U9vuAFI89SMc8PFGL2QCu2lIWELI/pNe0mfB6RbsvlmZOlcNB1kv6Fr\n3S/m4JQCS1Ne256R9WGX1aK2bXDD1V2yIZneCk39zsHQvJkVd5E+EEg3kTDLvqjR1eAGyJo+\nBAAOxJyPQestV08MdE0Gms7aSVj9BGo3vJdBL3YehvymXfX3oB8LJOOQXcmljk1ZYf+ceZ1u\n2GBvrCuPcLOf95DIA7v6yXut+HKnqnanwxHfkX4skNDDVnZttXqSXGYSdkkZGn3IWwZBwBKr\nW0m4aVy1ttPrsYmddYo/ayvq3doSVbvg+uKdo5hS6ccCSTEcTO3Yt7CKc625MHGjaIJesAF3\npIogRuTQ1oinU1781ryndAZjWOHfTK7vy2iv+A7M4vWR9GOBRBtHmRVl557ToZ70PmkAS9gw\nEvhYRIVkylNFfupVYeucgO21/QUbBe9R/2RP09C2lahaaK3xXmbcBfq5QJph44hMiYCONZns\nDS+WCjCy0IJ+XPfh3lLYoH64L9BmeEs27WtLuT0rlt+QfjSQ1szYUPE1kyHkSXsFpqBtR29B\nYXDGnbaxhTIrSGmrvVsx8g2B1G5RdN5QfDv64UCaTZSdO3CnXMsLKCzxIgm6lOeqKzjrbUAx\nsrD/yeAMrTRSjoT08uHYincrKTRRBeOKlM9J+jv6fhT9eCBpzc3JcxjQsMGSx/gptMt5gRPH\nwarDkZxPJlRpISPQUho+bkFfZHX9Lcrcln6BBNQymbGql6SFJY+N1CmdHzxyzGkAzHxRzwBQ\npzy/uoMzhID23QdmbH8Y8OPpF0hEbhVj3WPKIkpIQVvoHvdcuQBvauG8AKnz+wlYAqddB40s\ndFGHE/f4pUfQL5A8hJpdWSVgadZlE9yEte4ktQs7lSI6aWO9q7/K5/X59AskH2FOrhh7obHk\n1scGOxFv9GTl5aagsTyZa62MrBLV0eprtjM/n36B5CUqFNHqspCME6GGU5jmYhsSEF2GvSv7\ns0ULxorF8y+9kH6BFCAsqQe9ahFLbhFQoB8Oq3v4wdRAIAX0Vpf5r3nzXfQLpCBVRhRNzO5P\nU2am9kleuWoRK1ImTuHqRPEYat6oAspX0y+QwoRCqQhoYtLYUZknrmE0O/vn9pF6C6R9x4o/\nabzMx2qtv/QQ+gVSDHkS/yySbZV7QTJBXxfC0cngHbvUnWDjlWoD5ZG2f3/p4fQLJJZwg1V1\noURxw1QvGm1GHbuqYOMaWtw8km17uqRCbm1S5VykWrP6xaeWyw/5pXvpF0gsWbuxitwAWfhW\ntDbGnIX2IbxslRStOMfdCOiS7o85Ta6Tsk0qUvFu+X3fSb9A4mg64Ijx2SnjqPSX7lEqnarV\ncDbRgp6lSx5VzhChXsmgnuTSya3fX0qjXyBxhJpd2Q1S9tgmr+Sy9SykFXXPueysgv05lgk6\n9VJi0x7KHf1tOjKTFih+gfQM+gUSR6bJJe8cQwKE5JYamDMBQna3vexsW5MFFT1WWR7Vox1A\n6lVpMSwTidbdqUf9UhL9AomlNZnPiyXcQMpb2VWBQFLZqR0k0vFOvZPQ9VWwl0DtvM2ocGbA\n/W5ZSV9Jv0DyURSWqOQ3uKXHHnqysCGrVGUVa86Jk/ukLUIHd6MKLLvl0iRBCAn4vxax9HVd\nJt+SfoEUoMFUxud5nzaHaIdn8lV+vBxkSkBa93Wdvr9xFY3dacj+Uhr9AilMqt2mDwUU18AX\n4b+LWq3M1TyONJLerB3Fl9MvkKJowVLAOyCp68+DmdcACVqre/I1+roO+Eh+6V76BVIsBSGC\n7R29UGpBSMgKOjafpF9wvCv9AukitS1UOyQKuONq2NGhJmDPysjrq18b6Tn0C6RrdIx/YCUS\nFZEvfMbN7SSfCNqfTb9Aukb9AUjsqQ04/uD8qnheK+LmF0nPoV8gXSPwnuWijen5KmCHqQRp\nNDwxaqe+uefuL7npF0jXSEcqiHAjcgxKyGBTdnoakOSARYKLX6H0aPoF0jWqrSi7svHWPwEg\n1Rhkd7aEUAKZ3jFEv1myj6ZfIF2mwep679ttKhakkVevenQc6cFw+9oCp29Dv0C6hSTl0HoD\n22oVtwNREGw83j1kQagQTUS/2l+6Sr9Auo0w/ttX2CEnJav1+cjvIaEQxDRP+6X76RdIt9Lo\n5dxGVBLDfB69Sdr/IujZ9AukJ5Dc8HVb/Wpa30e/QHoCYQJe86tpfTP9AukJJGDz6Nf2/2r6\nBdITSNcGN/RbBP/r6BdITyE5T/b+6O+2ztfRL5CeSTre4Df47evoF0gvoF8b6fvoF0hPoLbt\n5G9u63fTL5CeQMI47H4d4N9Kv0B6AuW2xw7w9Cuevo5+gfQE+g3G/n76/07lVN+qRh6KAAAA\nAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Make the plot\n",
    "p <- ggplot(data) +      \n",
    "  \n",
    "  # Add the stacked bar\n",
    "  geom_bar(aes(x=as.factor(id), y=value, fill=observation), stat=\"identity\", alpha=0.5) +\n",
    "  scale_fill_viridis(discrete=TRUE) +\n",
    "  \n",
    "  # Add a val=100/75/50/25 lines. I do it at the beginning to make sur barplots are OVER it.\n",
    "  geom_segment(data=grid_data, aes(x = end, y = 0, xend = start, yend = 0), colour = \"grey\", alpha=1, size=0.3 , inherit.aes = FALSE ) +\n",
    "  geom_segment(data=grid_data, aes(x = end, y = 50, xend = start, yend = 50), colour = \"grey\", alpha=1, size=0.3 , inherit.aes = FALSE ) +\n",
    "  geom_segment(data=grid_data, aes(x = end, y = 100, xend = start, yend = 100), colour = \"grey\", alpha=1, size=0.3 , inherit.aes = FALSE ) +\n",
    "  geom_segment(data=grid_data, aes(x = end, y = 150, xend = start, yend = 150), colour = \"grey\", alpha=1, size=0.3 , inherit.aes = FALSE ) +\n",
    "  geom_segment(data=grid_data, aes(x = end, y = 200, xend = start, yend = 200), colour = \"grey\", alpha=1, size=0.3 , inherit.aes = FALSE ) +\n",
    "  \n",
    "  # Add text showing the value of each 100/75/50/25 lines\n",
    "  ggplot2::annotate(\"text\", x = rep(max(data$id),5), y = c(0, 50, 100, 150, 200), label = c(\"0\", \"50\", \"100\", \"150\", \"200\") , color=\"grey\", size=6 , angle=0, fontface=\"bold\", hjust=1) +\n",
    "  \n",
    "  ylim(-150,max(label_data$tot, na.rm=T)) +\n",
    "  theme_minimal() +\n",
    "  theme(\n",
    "    legend.position = \"none\",\n",
    "    axis.text = element_blank(),\n",
    "    axis.title = element_blank(),\n",
    "    panel.grid = element_blank(),\n",
    "    plot.margin = unit(rep(-1,4), \"cm\") \n",
    "  ) +\n",
    "  coord_polar() +\n",
    "  \n",
    "  # Add labels on top of each bar\n",
    "  geom_text(data=label_data, aes(x=id, y=tot+10, label=individual, hjust=hjust), color=\"black\", fontface=\"bold\",alpha=0.6, size=5, angle= label_data$angle, inherit.aes = FALSE ) +\n",
    "  \n",
    "  # Add base line information\n",
    "  geom_segment(data=base_data, aes(x = start, y = -5, xend = end, yend = -5), colour = \"black\", alpha=0.8, size=0.6 , inherit.aes = FALSE )  +\n",
    "  geom_text(data=base_data, aes(x = title, y = -18, label=group), hjust=c(1,1,0,0), colour = \"black\", alpha=0.8, size=4, fontface=\"bold\", inherit.aes = FALSE)\n",
    "\n",
    "p\n",
    "# 保存数据 Save at png\n",
    "ggsave(p, file=\"output.png\", width=10, height=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 参考\n",
    "+ [Circular barplot](https://www.r-graph-gallery.com/circular-barplot.html)\n",
    "+ [tidyverse](https://github.com/tidyverse)\n",
    "+ [R中rep函数的使用](https://www.cnblogs.com/business-analysis/p/3414997.html)\n",
    "+ [R语言中的管道%>%](https://www.jianshu.com/p/c65dbce983dd)\n",
    "+ [R语言 tidyr包的三个重要函数：gather，spread，separate的用法和举例](https://blog.csdn.net/six66667/article/details/84888644)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
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